Recent Submissions

  • Security and Performance Engineering of Scalable Cognitive Radio Networks. Sensing, Performance and Security Modelling and Analysis of ’Optimal’ Trade-offs for Detection of Attacks and Congestion Control in Scalable Cognitive Radio Networks

    Kouvatsos, Demetres D.; Chuku, Ejike E. (University of BradfordSchool of Engineering and Informatics, 2019)
    A Cognitive Radio Network (CRN) is a technology that allows unlicensed users to utilise licensed spectrum by detecting an idle band through sensing. How- ever, most research studies on CRNs have been carried out without considering the impact of sensing on the performance and security of CRNs. Sensing is essential for secondary users (SUs) to get hold of free band without interfering with the signal generated by primary users (PUs). However, excessive sensing time for the detection of free spectrum for SUs as well as extended periods of CRNs in an insecure state have adverse effects on network performance. Moreover, a CRN is very vulnerable to attacks as a result of its wireless nature and other unique characteristics such as spectrum sensing and sharing. These attacks may attempt to eavesdrop or modify the contents of packets being transmitted and they could also deny legitimate users the opportunity to use the band, leading to underutilization of the spectrum space. In this context, it is often challenging to differentiate between networks under Denial of Service (DoS) attacks from those networks experiencing congestion. This thesis employs a novel Stochastic Activity Network (SAN) model as an effective analytic tool to represent and study sensing vs performance vs security trade-offs in CRNs. Specifically, an investigation is carried out focusing on sensing vs security vs performance trade-offs, leading to the optimization of the spectrum band’s usage. Moreover, consideration is given either when a CRN experiencing congestion and or it is under attack. Consequently, the data delivery ratio (PDR) is employed to determine if the network is under DoS attack or experiencing congestion. In this context, packet loss probability, queue length and throughput of the transmitter are often used to measure the PDR with reference to interarrival times of PUs. Furthermore, this thesis takes into consideration the impact of scalability on the performance of the CRN. Due to the unpredictable nature of PUsactivities on the spectrum, it is imperative for SUs to swiftly utilize the band as soon as it becomes available. Unfortunately, the CRN models proposed in literature are static and unable to respond effectively to changes in service demands. To this end, a numerical simulation experiment is carried out to determine the impact of scalability towards the enhancement of nodal CRN sensing, security and performance. Atthe instant the band becomes idle and there are requests by SUs waiting for encryption and transmission, additional resources are dynamically released in order to largely utilize the spectrum space before the reappearance of PUs. These additional resources make the same service provision, such as encryption and intrusion detection, as the initial resources. To this end,SAN model is proposed in order to investigate the impact of scalability on the performance of CRN. Typical numerical simulation experiments are carried out, based on the application of the Mobius Petri Net Package to determine the performance of scalable CRNs (SCRNs) in comparison with unscalable CRNs (UCRNs) and associated interpretations are made.
  • Contributions to evaluation of machine learning models. Applicability domain of classification models

    Neagu, Daniel; Rado, Omesaad A.M. (University of BradfordFaculty of Engineering and Informatics, 2019)
    Artificial intelligence (AI) and machine learning (ML) present some application opportunities and challenges that can be framed as learning problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms have been successfully used in numerous classification problems. With the developing popularity of using ML models for many purposes in different domains, the validation of such predictive models is currently required more formally. Traditionally, there are many studies related to model evaluation, robustness, reliability, and the quality of the data and the data-driven models. However, those studies do not consider the concept of the applicability domain (AD) yet. The issue is that the AD is not often well defined, or it is not defined at all in many fields. This work investigates the robustness of ML classification models from the applicability domain perspective. A standard definition of applicability domain regards the spaces in which the model provides results with specific reliability. The main aim of this study is to investigate the connection between the applicability domain approach and the classification model performance. We are examining the usefulness of assessing the AD for the classification model, i.e. reliability, reuse, robustness of classifiers. The work is implemented using three approaches, and these approaches are conducted in three various attempts: firstly, assessing the applicability domain for the classification model; secondly, investigating the robustness of the classification model based on the applicability domain approach; thirdly, selecting an optimal model using Pareto optimality. The experiments in this work are illustrated by considering different machine learning algorithms for binary and multi-class classifications for healthcare datasets from public benchmark data repositories. In the first approach, the decision trees algorithm (DT) is used for the classification of data in the classification stage. The feature selection method is applied to choose features for classification. The obtained classifiers are used in the third approach for selection of models using Pareto optimality. The second approach is implemented using three steps; namely, building classification model; generating synthetic data; and evaluating the obtained results. The results obtained from the study provide an understanding of how the proposed approach can help to define the model’s robustness and the applicability domain, for providing reliable outputs. These approaches open opportunities for classification data and model management. The proposed algorithms are implemented through a set of experiments on classification accuracy of instances, which fall in the domain of the model. For the first approach, by considering all the features, the highest accuracy obtained is 0.98, with thresholds average of 0.34 for Breast cancer dataset. After applying recursive feature elimination (RFE) method, the accuracy is 0.96% with 0.27 thresholds average. For the robustness of the classification model based on the applicability domain approach, the minimum accuracy is 0.62% for Indian Liver Patient data at r=0.10, and the maximum accuracy is 0.99% for Thyroid dataset at r=0.10. For the selection of an optimal model using Pareto optimality, the optimally selected classifier gives the accuracy of 0.94% with 0.35 thresholds average. This research investigates critical aspects of the applicability domain as related to the robustness of classification ML algorithms. However, the performance of machine learning techniques depends on the degree of reliable predictions of the model. In the literature, the robustness of the ML model can be defined as the ability of the model to provide the testing error close to the training error. Moreover, the properties can describe the stability of the model performance when being tested on the new datasets. Concluding, this thesis introduced the concept of applicability domain for classifiers and tested the use of this concept with some case studies on health-related public benchmark datasets.
  • Multiuser Multi Input Single Output (MU-MISO) Beamforming for 5G Wireless and Mobile Networks. A Road Map for Fast and Low Complexity User Selection, Beamforming Scheme Through a MU-MISO for 5G Wireless and Mobile Networks

    Abd-Alhameed, Raed A.; Hameed, Khalid W.H. (University of BradfordFaculty of Engineering and Informatics, 2019)
    Multi-User Multi-Input Multi-Output (MU-MIMO) systems are considered to be the sustainable technologies of the current and future of the upcoming wireless and mobile networks generations. The perspectives of these technologies under several scenarios is the focus of the present thesis. The initial system model covers the MU-MIMO, especially in the massive form that is considered to be the promising ideas and pillars of the 5G network. It is observed that the optimal number of users should be served in the time-frequency resource even though the maximum limitation of the MU-MIMO is governed by the total receiving antennas (K) is less than or equal to the base station antennas (M). The system capacity of the massive MIMO (mMIMO) under perfect channel state information (CSI) of uncorrelated channel is investigated and studied. Two types of precoders were applied, one is directly based on channel inversion, and the other uses the Eigen decomposition that is derived subject to the signal to a leakage maximization problem. The two precoders show a degree of equivalency under certain assumptions for the number of antennas at the user end. The convex optimization of multi-antenna networks to achieve the design model of optimum beamformer (BF) based on the uniform linear array (ULA) is studied. The ULA is selected for its simplicity to analyse many scenarios and its importance to match the future network applied millimetre wave (mmWave) spectrum. The maximum beams generated by the ULA are explored in terms of several physical system parameters. The duality between the MU-MIMO and ULA and how they are related based on beamformer operation are detailed and discussed. Finally, two approaches for overloaded systems are presented when the availability of massive array that is not guaranteed due to physical restrictions since the existence of a large number of devices will result in breaking the dimension rule (i.e., K ≤ M). As a solution, a low complexity users selection algorithm is proposed. The channel considered is uncorrelated with full and perfect knowledge at the BS. In particular, these two channel conditions may not be available in all scenarios. The CSI may be imperfect, and even the instantaneous form does not exist. A hybrid precoder between the mixed CSI (includes imperfect and statistical) and rate splitting approach is proposed to deal with an overloaded system under a low number of BS antennas.
  • A qualitative exploration of autism and transition into further and higher education

    Rogers, Chrissie; Simmons, Amy L. (University of BradfordFaculty of Management, Law and Social Sciences, School of Social Sciences, 2019)
    In this thesis, I explore 42 autistic individuals’ transitions into further and higher education (FHE) in England, drawing on personal experience as well as interview data. I was diagnosed with Asperger’s Syndrome in 1998 at the age of 13. At the age of 15, my mother introduced the topic to me, and autism soon became the foundation of my socio-political identity. The discussion is divided into three themes; stigma and perception management strategies, formal and informal support networks and the interplay of autism with institutional factors. I draw upon Tringo’s (1970) work on the hierarchy of impairment and Goffman’s (1963) work on stigma. Tringo’s (1970) hierarchy of impairment led me to my intra-communal hierarchy of impairment (perpetuated by autistic individuals against autistic individuals) and Goffman’s (1963) work on stigma led me to my four degrees of openness; autistic individuals can be indiscriminately open, or indiscriminately reticent, but openness if relevant, and openness if necessary, are more common strategies. UPIAS’ (1976) work on the social model of disability laid the foundation for my socio-political identity and this thesis. I argue autism has been largely absent from the political arena. I outline how there are four ideals; the ideals of self-regulation, normalcy, ability and independence. Eager to conform to these ideals, eager to self-present as ‘independent’, ‘self-regulating’, ‘normal’ or ‘capable’, some autistic students are reluctant to request support and accommodations, complicating the transition to FHE.
  • A study of the relationships of power between humanitarian workers and local leaders in Haiti

    Kelly, Rhys H.S.; Quintiliani, Pierrette (University of BradfordFaculty of Social Sciences and Humanities, 2018)
    Like many former colonised countries, Haiti has been plagued by insecurity and conflicts caused by internal and external influences as well as natural disasters. In 1804, after a protracted conflict between slaves and French colonialists, Haiti became the first black country to gain its independence through a revolution. Today, Haiti is the poorest country in the Western hemisphere, ranking 153rd on the Human Development Index and a significant number of humanitarian organisations are present on the island aspiring at improving the standard of living of the population. The following study examines how the relationships of power emerging through the relationship between humanitarian and local leaders affect their perceptions of each other and identified the emotions emerging from these perceptions. The perceptions identified are the coloniality of power, corruption and distrust, the occurrence of conspiracy theories and the obstacles encountered in the implementation of a relief-development continuum model envisioned by general humanitarian policies. These perceptions create tensions between the humanitarian and local leaders, contributing to fuelling negative emotions such as regret, sadness, sense of failure, disappointment and anger. Negative emotions in this study affect the collaboration between humanitarians and local leaders, diminishing the positive influences and impact of humanitarian action on the well-being of the Haitian population. One of the components to increase these positive influences of humanitarian action is to lessen the asymmetricality of power between humanitarian and local leaders through the adoption of a Cultural Competence model by humanitarians.
  • Examining causal effects of Emotional Intelligence on human related challenges occurring in Agile managed Information Systems projects

    Sivarajah, Uthayasankar; Weerakkody, Vishanth J.P.; Luong, Tan T. (University of BradfordFaculty of Management and Law, 2020)
    Agile project management has become a widely implemented project management approach in Information Systems (IS). Yet, along with its growing popularity, the amount of concerns raised in regard to human related challenges is rapidly increasing. Nevertheless, the extant scholarly literature has neglected to identify the primary origins and reasons of these challenges. The purpose of this study is therefore to examine if these challenges are caused by a lack of Emotional Intelligence (EI) by means of a quantitative approach, which includes two main steps. Firstly, based on a sample of 447 IS-professionals, the psychometric properties of their EI in regard to their personal characteristics is examined. Secondly, based on the findings of the first analysis, the causal inference of EI on these challenges is computed using Propensity Score Matching based on a second sample of 194 agile practitioners. Different dimensions of EI were found to have a low to medium impact on human related challenges occurring in agile teams in regard to anxiety, motivation, mutual trust and communication competence. Hence, these findings offer important new knowledge for IS-scholars, project managers and human resource practitioners, about the vital role of EI for educating, staffing and training of IS-professionals working in agile teams.
  • Bond Performance between Corroded Steel and Recycled Aggregate Concrete Incorporating Nano Silica

    Ashour, Ashraf F.; Alhawat, Musab M. (University of BradfordFaculty of Engineering and Informatics, 2020)
    The current research project mainly aims to investigate the corrosion resistance and bond performance of steel reinforced recycled aggregate concrete incorporating nano-silica under both normal and corrosive environmental conditions. The experimental part includes testing of 180 pull-out specimens prepared from 12 different mixtures. The main parameters studied were the amount of recycled aggregate (RCA) (i.e. 0%, 25%, 50% and 100%), nano silica (1.5% and 3%), steel embedment length as well as steel bar diameter (12 and 20mm). Different levels of corrosion were electrochemically induced by applying impressed voltage technique for 2, 5, 10 and 15 days. The experimental observations mainly focused on the corrosion level in addition to the ultimate bond, failure modes and slips occurred. Experimental results showed that the bond performance between un-corroded steel and recycled aggregate concrete slightly reduced, while a significant degradation was observed after being exposed to corrosive conditions, in comparison to normal concrete. On the other hand, the use of nano silica (NS) showed a reasonable bond enhancement with both normal and RCA concretes under normal conditions. However, much better influence in terms of bond and corrosion resistance was observed under advancing levels of corrosion exposure, reflecting the improvement in corrosion resistance. Therefore, NS was superbly effective in recovering the poor performance in bond for RCA concretes. More efficiency was reported with RCA concretes compared to the conventional concrete. The bond resistance slightly with a small amount of corrosion (almost 2% weight loss), then a significant bond degradation occurs with further corrosion. The influence of specific surface area and amount of nano silica on the performance of concrete with different water/binder (w/b) ratios has been also studied, using 63 different mixtures produced with three different types of colloidal NS having various surface areas and particle sizes. The results showed that the performance of concrete is heavily influenced by changing the surface area of nano silica. Amongst the three used types of nano silica, NS with SSA of 250 m2 /g achieved the highest enhancement rate in terms of compressive strength, water absorption and microstructure analysis, followed by NS with SSA of 500 m2/g, whilst NS with SSA of 51.4 m2 /g was less advantageous for all mixtures. The optimum nano silica ratio in concrete is affected by its particle size as well as water to binder ratio. The feasibility of the impact-echo method for identifying the corrosion was evaluated and compared to the corrosion obtained by mass loss method. The results showed that the impact-echo testing can be effectively used to qualitatively detect the damage caused by corrosion in reinforced concrete structures. A significant difference in the dominant frequencies response was observed after exposure to the high and moderate levels of corrosion, whilst no clear trend was observed at the initial stage of corrosion. Artificial neural network models were also developed to predict bond strength for corroded/uncorroded steel bars in concrete using the main influencing parameters (i.e., concrete strength, concrete cover, bar diameter, embedment length and corrosion rate). The developed models were able to predict the bond strength with a high level of accuracy, which was confirmed by conducting a parametric study.
  • The Construction of Care in Computed Tomography. Exploring Care from the Perspective of Patients and Radiographers

    Not given; Forton, Rachael K. (University of BradfordFaculty of Health Studies, 2019)
    Purpose: Patient centred care and the ‘patient voice’ are core components of UK healthcare policy and practice guidance. This study explores how care is perceived and experienced within the high technology environment of CT. Methods and Materials: A two-phase approach of Critical Discourse Analysis (CDA) and adapted Grounded Theory (GT) methodology using semi structured interviews, was used to obtain primary data from CT radiographers and patients. Recruitment and data collection were performed at a 1200 bed teaching hospital over a 6-month period. Results: The radiographer patient relationship and the radiographer’s role in providing care within CT are complex and multifaceted. Both patients and radiographer’s perceive CT imaging to be an integral part of the overall patient care and treatment pathway. As such, the act of being imaged is perceived as a care process and while image acquisition is recognised as a task orientated and technical process, the human element of providing care is cognitive, dynamic and responsive to individual need. Importantly, patient confidence in the care received was influenced by the radiographer’s ability to build a trusting relationship and display technical competence and this in turn facilitated active compliance resulting in a technically accurate examination. Despite previous literature suggesting that the technical environment created a barrier to patient care, patients within this study confirmed that radiographers provide care commensurate to the nursing ideals represented by the 6C’s (Care; Compassion; Competence; Communication; Courage; Commitment). Conclusions: A co-constructed model of care encompassing both technical components and patient-centeredness has been identified. This model promotes a new vision of patient centred care based on care perceptions within the high technology environment of CT.
  • Investigation of Integrated Decoupling Methods for MIMO Antenna Systems. Design, Modelling and Implementation of MIMO Antenna Systems for Different Spectrum Applications with High Port-to-Port Isolation Using Different Decoupling Techniques

    Abd-Alhameed, Raed A.; Excell, Peter S.; McEwan, Neil J.; Noras, James M.; Salah, Adham M.S. (University of BradfordFaculty of Engineering and Informatics, 2019)
    Multiple-Input-Multiple-Output (MIMO) antenna technology refers to an antenna with multiple radiators at both transmitter and receiver ends. It is designed to increase the data rate in wireless communication systems by achieving multiple channels occupying the same bandwidth in a multipath environment. The main drawback associated with this technology is the coupling between the radiating elements. A MIMO antenna system merely acts as an antenna array if the coupling between the radiating elements is high. For this reason, strong decoupling between the radiating elements should be achieved, in order to utilize the benefits of MIMO technology. The main objectives of this thesis are to investigate and implement several printed MIMO antenna geometries with integrated decoupling approaches for WLAN, WiMAX, and 5G applications. The characteristics of MIMO antenna performance have been reported in terms of scattering parameters, envelope correlation coefficient (ECC), total active reflection coefficient (TARC), channel capacity loss (CCL), diversity gain (DG), antenna efficiency, antenna peak gain and antenna radiation patterns. Three new 2×2 MIMO array antennas are proposed, covering dual and multiple spectrum bandwidths for WLAN (2.4/5.2/5.8 GHz) and WiMAX (3.5 GHz) applications. These designs employ a combination of DGS and neutralization line methods to reduce the coupling caused by the surface current in the ground plane and between the radiating antenna elements. The minimum achieved isolation between the MIMO antennas is found to be better than 15 dB and in some bands exceeds 30 dB. The matching impedance is improved and the correlation coefficient values achieved for all three antennas are very low. In addition, the diversity gains over all spectrum bands are very close to the ideal value (DG = 10 dB). The forth proposed MIMO antenna is a compact dual-band MIMO antenna operating at WLAN bands (2.4/5.2/5.8 GHz). The antenna structure consists of two concentric double square rings radiating elements printed symmetrically. A new method is applied which combines the defected ground structure (DGS) decoupling method with five parasitic elements to reduce the coupling between the radiating antennas in the two required bands. A metamaterial-based isolation enhancement structure is investigated in the fifth proposed MIMO antenna design. This MIMO antenna consists of two dual-band arc-shaped radiating elements working in WLAN and Sub-6 GHz 5th generation (5G) bands. The antenna placement and orientation decoupling method is applied to improve the isolation in the second band while four split-ring resonators (SRRs) are added between the radiating elements to enhance the isolation in the first band. All the designs presented in this thesis have been fabricated and measured, with the simulated and measured results agreeing well in most cases.
  • Machine Learning-based Feature Selection and Optimisation for Clinical Decision Support Systems. Optimal Data-driven Feature Selection Methods for Binary and Multi-class Classification Problems: Towards a Minimum Viable Solution for Predicting Early Diagnosis and Prognosis

    Neagu, Daniel; Campean, I. Felician; Parisi, Luca (University of BradfordFaculty of Engineering and Informatics, 2019)
    This critical synopsis of prior work by Luca Parisi is submitted in support of a PhD by Published Work. The work focuses on deriving accurate, reliable and explainable clinical decision support systems as minimum clinically viable solutions leveraging Machine Learning (ML) and evolutionary algorithms, for the first time, to facilitate early diagnostic predictions of Parkinson's Disease and hypothermia in hospitals, as well as prognostic predictions of optimal postoperative recovery area and of chronic hepatitis. Despite the various pathological aetiologies, the underlying capability of ML-based algorithms to serve as a minimum clinically viable solution for predicting early diagnosis and prognosis has been thoroughly demonstrated. Feature selection (FS) is a proven method for increasing the performance of ML-based classifiers for several applications. Although advances in ML, such as Deep Learning (DL), have denied the usefulness of any extrinsic FS by incorporating it in their architectures, e.g., convolutional filters in convolutional neural networks, DL algorithms often lack the required explainability to be understood and interpreted by clinicians within the context of the diagnostic and prognostic tasks of interest. Their relatively complicated architectures, the hardware required for running them and the limited explainability or interpretability of their architectures, the decision-making process – although as assistive tools - driven by the algorithms’ training and predictive outcomes have hindered their application in a clinical setting. Luca Parisi’s work fills this translational research gap by harnessing the explainability of using traditional ML- and evolutionary algorithms-based FS methods for improving the performance of ML-based algorithms and devise minimum viable solutions for diagnostic and prognostic purposes. The work submitted here involves independent research work, including collaborative studies with Marianne Lyne Manaog (MedIntellego®) and Narrendar RaviChandran (University of Auckland). In particular, conciliating his work as a Senior Artificial Intelligence Engineer and volunteering commitment as the President and Research Committee Leader of a student-led association named the “University of Auckland Rehabilitative Technologies Association”, Luca Parisi decided to embark on most research works included in this synopsis to add value to society via accurate, reliable and explainable, hence clinically viable applications of AI. The key findings of these studies are: (i) ML-based FS algorithms are sufficient for devising accurate, reliable and explainable ML-based classifiers for aiding prediction of early diagnosis for Parkinson’s Disease and chronic hepatitis; (ii) evolutionary algorithms-based optimisation is a preferred method for improving the accuracy and reliability of decision support systems aimed at aiding early diagnosis of hypothermia; (iii) evolutionary algorithms-based optimisation methods enable to devise optimised ML-based classifiers for improving postoperative discharge; (iv) whilst ML-based algorithms coupled with ML based FS methods are the minimum clinically viable solution for binary classification problems, ML-based classifiers leveraging evolutionary algorithms for FS yield more accurate and reliable predictions, as reducing the search space and overlapping regions for tackling multi-class classification problems more effectively, which involve a higher number of degrees of freedom. Collectively, these findings suggest that, despite advances in ML, state-of-the-art ML algorithms, coupled with ML-based or evolutionary algorithms for FS, are enough to devise accurate, reliable and explainable decision support systems for performing both an early diagnosis and a prediction of prognosis of various pathologies.
  • The Impact of Training in Person-Centred Dementia Care and Supervision on Burnout in Nursing Home Nurses: A Mixed Methods Study

    Oyebode, Jan R.; Downs, Murna G.; Smythe, Analisa (University of BradfordFaculty of Health Studies, 2018)
    Background: There is significant concern about nurse burnout in nursing homes. There has been little research to investigate whether training in person-centred care and supervision can reduce nursing home nurses’ burnout. Aims: To adapt training to be suitable for nursing home nurses and evaluate the impact of training and supervision on burnout and related outcomes. Study Design: Focus groups with nursing home nurses were used to inform adaptation of the training. Mixed methods were used to evaluate the impact of training and supervision employing a convergent parallel design, including a Randomised Controlled Trial with quantitative measures (primary outcome measure: the Maslach Burnout Inventory) to assess effectiveness and exploration of subjective experience using qualitative interviews. The findings of the RCT and qualitative interviews were then compared to determine the convergences and divergences. Findings: The training was adapted to include content on leadership and stress management. Hypotheses that the interventions would reduce burnout and impact on other quantitative outcomes were not supported. Qualitative interviews with nursing home nurses about training indicated that the nurses reported reduced burnout, enhanced self-efficacy, reduced isolation, better team working, more informed person centred dementia care and enhanced leadership. Nurses’ views on the impact of supervision included a range of benefits. There was convergence between quantitative measurement and subjective experience indicting significant levels of burnout, but divergence in terms of the impact of training in person-centred care and supervision. Conclusions: My study demonstrates that burnout is a significant issue for nursing home nurses in the UK. There was divergence in my findings in terms of the impact of training in person-centred care and supervision. The hypotheses about training and supervision having positive impact on burn-out were rejected. However, the qualitative findings suggest that nursing home nurses experienced positive benefits from the person-centred training and supervision, in particular on their sense of burnout, their approach to care and leadership skills. Recommendations are made regarding research, training and policy to address burnout in nursing home nurses.
  • Creation of controlled polymer extrusion prediction methods in fused filament fabrication. An empirical model is presented for the prediction of geometric characteristics of polymer fused filament fabrication manufactured components

    Whiteside, Benjamin R.; Coates, Philip D.; Caton-Rose, Philip D.; Hebda, Michael J. (University of BradfordFaculty of Engineering and Informatics, 2019)
    This thesis presents a model for the procedures of manufacturing Fused Filament Fabrication (FFF) components by calculating required process parameters using empirical equations. Such an empirical model has been required within the FFF field of research for a considerable amount of time and will allow for an expansion in understanding of the fundamental mathematics of FFF. Data acquired through experimentation has allowed for a data set of geometric characteristics to be built up and used to validate the model presented. The research presented draws on previous literature in the fields of additive manufacturing, machine engineering, tool-path programming, polymer science and rheology. Combining these research fields has allowed for an understanding of the FFF process which has been presented in its simplest form allowing FFF users of all levels to incorporate the empirical model into their work whilst still allowing for the complexity of the process. Initial literature research showed that Polylactic Acid (PLA) is now in common use within the field of FFF and therefore was selected as the main working material for this project. The FFF technique, which combines extrusion and Computer Aided Manufacturing (CAM) techniques, has a relatively recent history with little understood about the fundamental mathematics governing the process. This project aims to rectify the apparent gap in understanding and create a basis upon which to build research for understanding complex FFF techniques and/or processes involving extruding polymer onto surfaces.
  • By the Head of a Spirited Horse: A Biocultural Analysis of Horse-Depositions as Reflections of Horseman Identities in Early Britain (Iron Age to Early Medieval Period)

    Not given; Cross, Pamela J. (University of BradfordFaculty of Life Sciences, School of Archaeological and Forensic Sciences, 2018)
  • Open Innovation Practices and Innovation Performance: A Dynamic Capabilities Approach

    Wang, Chengang; Pillai, Kishore G.; Ovuakporie, Oghogho D. (University of BradfordFaculty of Management, Law and Social Sciences, 2018)
  • An Experimental Study on the Effects of Heat and Chemical Inhibitors on the Flow Behaviour of Waxy Crude Oils. The Effects of Heat and Chemical Inhibitors on the Rheological Properties of Waxy Crude Oils with regard to Pumping in Pipelines

    Benkreira, Hadj; Al-Hengari, S.; Mohamed, Fathia A.B. (University of BradfordFaculty of Engineering and Informatics, 2019)
    Waxy crude oils (1/3 of oil produced worldwide), pumping through pipelines considered risky operation due to the crude wax content (15-40 wt.%) and to the temperature at which wax supersaturates and precipitates, leading to the danger of pipe blockage, eventually resulting, in multimillion dollars loss in production and maintenance. This research undertaken to develop operational strategy of waxy crude pipelines, considering the crude and crude gel properties and flow conditions. The research problem was approached by characterizing the crude gel with and without additives using chromatography (GC), differential scanning calorimetry (DSC), cross polarised microscopy (CPM), controlled stress and oscillatory shear rheology (CSR and OSR), the principal parameters being the crude temperature and the rate at which the crude was cooled. GC and DSC were useful in establishing wax composition, content and wax appearance temperature (WAT). Control stress rheometer proved to be the most appropriate as it measured the reduction in apparent viscosity at full production (10-50 s-1 shear rate), near shutdown (1 s-1 ) and yielding when the oil was statically cooled. On this basis, it was established that the wax inhibitor was the most effective. CPM revealed that only the wax inhibitor changed the structure of the gel, disrupting its otherwise knitted crystal network. Dilution with the light crude oil merely reduced the wax content and the pour point depressant reduced the gelling temperature. OSR provided a check on CSR and confirmed the gelation temperature measured. CSR provided the yield stress measured, it also provided comprehensive data that can be used for theoretical modelling of this complex flow.
  • Behaviour of continuous concrete deep beams reinforced with GFRP bars

    Ashour, Ashraf F.; Sheehan, Therese; Shalookh, Othman H. Zinkaah (University of BradfordFaculty of Engineering and Informatics, 2019)
  • Discourses of Power and Representation in British Broadcasting Corporation Documentary Practices: 1999-2013

    Not given; Thornton, Karen D. (University of BradfordFaculty of Engineering and Informatics, 2018)
    This dissertation re-evaluates the ways in which contemporary television documentary practices engage their audience. Bringing together historical frameworks, and using them to analyse a range of examples not considered together within this context previously, the main finding is that the use of spectacle to engage the audience into a visceral response cuts across all of the examples analysed, regardless of the subject matter being explored. Drawing on a media archaeological approach, the dissertation draws parallels with the way in which pre-cinema engaged an audience where the primary point of engagement came from the image itself, rather than a narrative. Within a documentary context, which is generally understood as a genre which is there to educate or inform an audience, the primacy of spectacle calls for a re-evaluation of the form and function of documentary itself. Are twenty-first century documentary practices manufacturing an emotional connection to engage the audience over attempting to persuade with reasoning and logic? The answer contained within this dissertation is that they are.
  • Big Social Data Analytics: A Model for the Public Sector

    Kamala, Mumtaz A.; Tassabehji, Rana; Bin Saip, Mohamed A. (University of BradfordFaculty of Engineering and Informatics, 2019)
    The influence of Information and Communication Technologies (ICTs) particularly internet technology has had a fundamental impact on the way government is administered, provides services and interacts with citizens. Currently, the use of social media is no longer limited to informal environments but is an increasingly important medium of communication between citizens and governments. The extensive and increasing use of social media will continue to generate huge amounts of user-generated content known as Big Social Data (BSD). The growing body of BSD presents innumerable opportunities as well as challenges for local government planning, management and delivery of public services to citizens. However, the governments have not yet utilised the potential of BSD for better understanding the public and gaining new insights from this new way of interactions. Some of the reasons are lacking in the mechanism and guidance to analyse this new format of data. Thus, the aim of this study is to evaluate how the body of BSD can be mined, analysed and applied in the context of local government in the UK. The objective is to develop a Big Social Data Analytics (BSDA) model that can be applied in the case of local government. Data generated from social media over a year were collected, collated and analysed using a range of social media analytics and network analysis tools and techniques. The final BSDA model was applied to a local council case to evaluate its impact in real practice. This study allows to better understand the methods of analysing the BSD in the public sector and extend the literature related to e-government, social media, and social network theory
  • Mechanical, thermal and acoustic properties of rubberised concrete incorporating nano silica

    Ashour, Ashraf F.; Khan, Amir; Dai, Xianghe; El-Khoja, Amal M.N. (University of BradfordFaculty of Engineering and Informatics, 2019)
    Very limited research studies have been conducted to examine the behaviour of rubberised concrete (RuC) with nano silica (NS) and addressed the acoustic benefits of rubberised concrete. The current research investigates the effect of incorporating colloidal nano silica on the mechanical, thermal and acoustic properties of Rubberised concrete and compares them with normal concrete (NC). Two sizes of rubber were used RA (0.5 – 1.5 mm) and RB (1.5 – 3 mm). Fine aggregate was replaced with rubber at a ratio of 0%, 10%, 20% and 30% by volume, and NS is used as partial cement replacement by 0%, 1.5% and 3%. A constant water to cement ratio of 0.45 was used in all concrete mixes. Various properties of rubberised concrete, including the density, water absorption, the compressive strength, the flexural strength, splitting tensile strength and the drying shrinkage of samples was studied as well as thermal and acoustic properties. Experimental results of compressive strength obtained from this study together with collected comprehensive database from different sources available in the literature were compared to five existing models, namely Khatib and Bayomy- 99 model, Guneyisi-04 model, Khaloo-08 model, Youssf-16 model, and Bompa-17 model. To assess the quality of predictive models, influence of rubber content on the compressive strength is studied. An artificial neural network (ANN) models were developed to predict compressive strength of RuC using the same data used in the existing models. Three ANN sets namely ANN1, ANN2 and ANN3 with different numbers of hidden layer neurons were constructed. Comparison between the results given by the ANN2 model and the results obtained by the five existing predicted models were presented. A finite element approach is proposed for calculating the transmission loss of concrete, the displacement in the solid phase and the pressure in the fluid phase is investigated. The transmission loss of the 50mm concrete samples is calculated via the COMSOL environment, the results from the simulation show good agreement with the measured data. The results showed that, using up to 20% of rubber as fine aggregate with the addition of 3% NS can produce a higher compressive strength than the NC. Experimental results of this research indicate that incorporating nano silica into RuC mixes enhance sound absorption and thermal conductivity compared to normal concrete (NC) and rubberised concrete without nano silica. This work suggests that it is possible to design and manufacture concrete which can provide an improvement to conventional concrete in terms of the attained vibro-acoustic and thermal performance.
  • Biomarkers of Genotoxic and Reprotoxic Effects after Chemical Exposure. The genotoxic effects due to the respiratory disease of Tuberculosis (TB) patients compared to healthy controls in diploid lymphocyte and haploid sperm cells, after treated with two heterocyclic amines and quercetin in bulk and nano forms

    Anderson, Diana; Gopalan, Rajendran C.; Abdulmwli, Mhamoued A.A. (University of BradfordSchool of Chemistry and Bioscience, 2019)
    In the tuberculosis patients, Mycobacterium tuberculosis can stimulate production of hydrogen peroxide in the host as a result of immune response. The H2O2 accumulate in pulmonary cells, causing oxidative stress that could lead to the cancer. We select TB patients for this study which investigates the effects of quercetin as there is an increased incidence of latent TB among the migrant population in the past few years and TB can increase the risk of cancer. Sperm and lymphocytes were treated with DNA damage inducers and quercetin (10µM, 25µM and 100µM), the responses evaluated using the Comet and micronucleus techniques. The gene expressions of COX1, COX2, P53 and Bcl-2 and catalase protein expression were investigated using the qPCR and Western blot techniques. The results showed that a substantial reduction of DNA damage in lymphocytes from TB patients and sperm from healthy donors from * P ≤ 0.0283 to *** P≤0.001in the Comet assay. In the MNi assay, the effect of quercetin in lymphocytes was more significant in reduce DNA damage, whereas the DNA damage induced by a food mutagen was significant, from *p 0.0405 to ***p 0.001. The qPCR showed significance down-regulation of COX1 and Bcl-2 gene expression, rated between *p 0.045 and **p 0.0074. However, the catalase protein was up-regulated by the nano form of quercetin when using lymphocytes from TB patients and showed significant changes at *p 0.0236. In conclusion, the nano form was found to be more efficient at the reduction of DNA damage in the Comet and micronucleus assays. Also, it down-regulated COX1 and Bcl-2 and up-regulated the catalase proteins indicating a possible role for quercetin, in genoprotection to TB through its enzyme modulating effect.

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