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Development of novel tumour-targeted gold nanocages for cancer therapy
No full textThis thesis was previously under moratorium from 21/02/2022 to 21/02/2024
'I shall have to learn to live all over again’: injury, disability and relationships in the lives of Second World War servicemen
Get PDFThis thesis was previously held under moratorium from 1st June 2022 until 1st June 2024.In this thesis I will examine the experiences of servicemen who were injured and disabled by service in the armed forces during the Second World War. I follow the serviceman from the site of injury to hospitalisation, rehabilitation and finally to life after war service. In addition, I explore the relationships servicemen formed at each stage of their recovery and what these meant to men. Throughout this thesis I adopt a person-centred approach by focusing on the actual lived experiences of servicemen, the nurses who cared for them and the women who entered relationships with them. In doing so, I make use of a range of sources such as my own interviews, previously conducted oral histories, private papers, hospital magazines and pension records. The main themes of the thesis are gender, masculinity, disability, and relationships. Relationships presents itself as a dominant theme throughout this thesis and is its most original finding. Interactions and relationships played a role in men’s experiences at every stage of their journey, from injury to hospital and surgery to rehabilitation and finally finding work as a disabled man. The importance of men’s relationships appears in several different contexts, friendship with comrades, sex and intimacy, shame, and embarrassment from public reactions to them and the opportunities afforded to them in the workplace. Interactions and relationships ultimately shaped men’s experience and memory of wartime disability. Those men who had strong support systems in the form of surgeons, comrades, romantic partners, family, and work had more positive experiences of wartime disability.In this thesis I will examine the experiences of servicemen who were injured and disabled by service in the armed forces during the Second World War. I follow the serviceman from the site of injury to hospitalisation, rehabilitation and finally to life after war service. In addition, I explore the relationships servicemen formed at each stage of their recovery and what these meant to men. Throughout this thesis I adopt a person-centred approach by focusing on the actual lived experiences of servicemen, the nurses who cared for them and the women who entered relationships with them. In doing so, I make use of a range of sources such as my own interviews, previously conducted oral histories, private papers, hospital magazines and pension records. The main themes of the thesis are gender, masculinity, disability, and relationships. Relationships presents itself as a dominant theme throughout this thesis and is its most original finding. Interactions and relationships played a role in men’s experiences at every stage of their journey, from injury to hospital and surgery to rehabilitation and finally finding work as a disabled man. The importance of men’s relationships appears in several different contexts, friendship with comrades, sex and intimacy, shame, and embarrassment from public reactions to them and the opportunities afforded to them in the workplace. Interactions and relationships ultimately shaped men’s experience and memory of wartime disability. Those men who had strong support systems in the form of surgeons, comrades, romantic partners, family, and work had more positive experiences of wartime disability
Essays on investment in carbon capture technology: the role of markets, competitors and policy
Get PDFIn this thesis, three different economic models under an industrial organization approach are presented modelling different types of carbon capture technology adoption. The thesis aims to understand the incentives that drive a carbon capture technology decision making at a firm level and develop policy solutions to inform government and policymakers to increase carbon capture technology adoption. The first model constructed considers a carbon capture and storage (CCS) technology adoption in different competitive environments. The focus is to explore how competition influences a firm’s decision toward CCS technology. The second model investigates the strategic interaction that firms experience in an industry where a firm adopts carbon capture and CO2 utilization (CCU). The model also evaluates the environmental impact of a CCU industry, as a major drawback of final goods produced by CO2 utilization is the carbon emissions are released back into the atmosphere once consumed in the final goods market. In this chapter, a series of policy solutions are proposed to obtain an increase in the adoption of CCU whilst accomplishing a positive environmental impact. The third model investigates the optimal CCS adoption decision time of a follower influenced by a learning-by-doing and spillover effect. A follower is a firm that adopts a second-generation CCS technology with a lower production cost caused by a learning effect from a pioneer. A pioneer is a firm that adopts a first-generation CCS technology with a high production cost, and it experiences a learning-by-doing effect. We discover, that if the adoption of CCS technology is sequential, a pioneer is always at an economic disadvantage by adopting first. The main contribution of this chapter recommends a policy solution that balances the adoption cost of a pioneer and a follower, achieving an increase in the diffusion of CCS technology.In this thesis, three different economic models under an industrial organization approach are presented modelling different types of carbon capture technology adoption. The thesis aims to understand the incentives that drive a carbon capture technology decision making at a firm level and develop policy solutions to inform government and policymakers to increase carbon capture technology adoption. The first model constructed considers a carbon capture and storage (CCS) technology adoption in different competitive environments. The focus is to explore how competition influences a firm’s decision toward CCS technology. The second model investigates the strategic interaction that firms experience in an industry where a firm adopts carbon capture and CO2 utilization (CCU). The model also evaluates the environmental impact of a CCU industry, as a major drawback of final goods produced by CO2 utilization is the carbon emissions are released back into the atmosphere once consumed in the final goods market. In this chapter, a series of policy solutions are proposed to obtain an increase in the adoption of CCU whilst accomplishing a positive environmental impact. The third model investigates the optimal CCS adoption decision time of a follower influenced by a learning-by-doing and spillover effect. A follower is a firm that adopts a second-generation CCS technology with a lower production cost caused by a learning effect from a pioneer. A pioneer is a firm that adopts a first-generation CCS technology with a high production cost, and it experiences a learning-by-doing effect. We discover, that if the adoption of CCS technology is sequential, a pioneer is always at an economic disadvantage by adopting first. The main contribution of this chapter recommends a policy solution that balances the adoption cost of a pioneer and a follower, achieving an increase in the diffusion of CCS technology
Assessing policy influence : trade unions, workplace learning, and the skills agenda
Get PDFThis thesis evaluates the effectiveness of trade union influence in the policy process, with particular focus on policy debates on skills and learning. It a key objective of trade unions to influence government policy. Unions seek to exert influence in the industrial and political spheres, in order to effectively represent the interests of their members and those of wider society. As their power in the industrial sphere has declined in recent decades and their influence narrowed, unions have looked to broaden their activities, giving increasing attention to government and the public policy arena and the ways in which they can influence key decision-makers. Against this backdrop, this thesis seeks to assess trade union influence on the policy process and consider the extent to which unions’ engagement in workplace learning and skills initiatives has increased their influence on the State.This thesis draws on the tools for measuring policy influence found in the political science literature and the debates within the industrial relations literature that seek to examine trade unions’ relationship with the State. A model has been developed to assess the STUC’s influence on the policy process, and takes account of their policy priorities in learning and skills, the tactics they employ to exert influence, and which outcomes they have achieved. This research also considers whether influence on skills and learning has led to broader policy influence.Data is drawn primarily from a single case study and 27 in-depth interviews. The research highlights the complexities of assessing policy influence, and uncovers the more nuanced forms largely overlooked in the existing literature. These less visible manifestations of influence uncover new insights into the ways in which unions try to achieve their policy priorities and moves beyond outcomes as a proxy for influence.This thesis evaluates the effectiveness of trade union influence in the policy process, with particular focus on policy debates on skills and learning. It a key objective of trade unions to influence government policy. Unions seek to exert influence in the industrial and political spheres, in order to effectively represent the interests of their members and those of wider society. As their power in the industrial sphere has declined in recent decades and their influence narrowed, unions have looked to broaden their activities, giving increasing attention to government and the public policy arena and the ways in which they can influence key decision-makers. Against this backdrop, this thesis seeks to assess trade union influence on the policy process and consider the extent to which unions’ engagement in workplace learning and skills initiatives has increased their influence on the State.This thesis draws on the tools for measuring policy influence found in the political science literature and the debates within the industrial relations literature that seek to examine trade unions’ relationship with the State. A model has been developed to assess the STUC’s influence on the policy process, and takes account of their policy priorities in learning and skills, the tactics they employ to exert influence, and which outcomes they have achieved. This research also considers whether influence on skills and learning has led to broader policy influence.Data is drawn primarily from a single case study and 27 in-depth interviews. The research highlights the complexities of assessing policy influence, and uncovers the more nuanced forms largely overlooked in the existing literature. These less visible manifestations of influence uncover new insights into the ways in which unions try to achieve their policy priorities and moves beyond outcomes as a proxy for influence
Development of a clinically-targeted human activity recognition system to aid the prosthetic rehabilitation of individuals with lower limb amputation in free living conditions
Get PDFAim: Healthcare Professionals (HCPs) that specialize in the care of Individuals with Lower Limb Amputation (ILLAs) typically evaluate the patient’s physical wellbeing through physical function tests or subjective questionnaires filled out by the patient. These evaluations give a limited understanding of the ILLA’s physical wellbeing, which can be evaluated more in-depth via wearable sensor-based Human Activity Recognition (HAR) of physical activities. The key objectives of this thesis were to determine which physical activities could be of interest to HCPs, develop a portable sensor-based system to capture those physical activities, then evaluate how reliably those activities could be captured with wearable sensors.Methodology: A focus group was conducted with ILLAs and HCPs to identify the relevant outcome measurements for clinical assessment. A novel HAR study was conducted with ILLAs and non-amputated individuals wearing a thigh-bound accelerometer (ActivPAL™, PAL Technologies, Glasgow, UK) to evaluate how reliably these outcome measurements could be captured in free-living conditions.Results: The key activity monitoring outcomes identified were walking activities on a variety of terrains. Using supervised machine learning, a Support Vector Machine could capture walking activities on flat terrain, walking on hills and walking on stairs. There was further potential to distinguish the activities on walking terrains based on whether they were hard or soft. With unsupervised machine learning, it was possible to distinguish walking on flat or sloping terrain with walking up and down stairs without the need for annotated training data using a novel formula-based algorithm. The ActivPAL proprietary algorithm was also validated for detecting walking and stationary activity of ILLAs in free-living conditions.Conclusion: The thesis validated an activity monitoring system that could capture a variety of walking activities performed by ILLAs. These findings form the basis of a clinical activity monitoring framework which would allow HCPs to monitor the walking activity of their patients and gain a greater understanding of their rehabilitation progress.Aim: Healthcare Professionals (HCPs) that specialize in the care of Individuals with Lower Limb Amputation (ILLAs) typically evaluate the patient’s physical wellbeing through physical function tests or subjective questionnaires filled out by the patient. These evaluations give a limited understanding of the ILLA’s physical wellbeing, which can be evaluated more in-depth via wearable sensor-based Human Activity Recognition (HAR) of physical activities. The key objectives of this thesis were to determine which physical activities could be of interest to HCPs, develop a portable sensor-based system to capture those physical activities, then evaluate how reliably those activities could be captured with wearable sensors.Methodology: A focus group was conducted with ILLAs and HCPs to identify the relevant outcome measurements for clinical assessment. A novel HAR study was conducted with ILLAs and non-amputated individuals wearing a thigh-bound accelerometer (ActivPAL™, PAL Technologies, Glasgow, UK) to evaluate how reliably these outcome measurements could be captured in free-living conditions.Results: The key activity monitoring outcomes identified were walking activities on a variety of terrains. Using supervised machine learning, a Support Vector Machine could capture walking activities on flat terrain, walking on hills and walking on stairs. There was further potential to distinguish the activities on walking terrains based on whether they were hard or soft. With unsupervised machine learning, it was possible to distinguish walking on flat or sloping terrain with walking up and down stairs without the need for annotated training data using a novel formula-based algorithm. The ActivPAL proprietary algorithm was also validated for detecting walking and stationary activity of ILLAs in free-living conditions.Conclusion: The thesis validated an activity monitoring system that could capture a variety of walking activities performed by ILLAs. These findings form the basis of a clinical activity monitoring framework which would allow HCPs to monitor the walking activity of their patients and gain a greater understanding of their rehabilitation progress
Real-time rheological investigation of complex behaviour in cornstarch suspensions
No full textCornstarch suspensions are well known for their surprising property of being able to support a person running across the surface of a pool of the suspension — a phenomenon called shear jamming. This phenomenon has far reaching importance across industries involving granular and colloidal solids transport. In this thesis, the design of a novel custom rheometer for the investigation of these suspensions is described. The results of applying the custom rheometer are shown and analysed in the context of the popular Wyart-Cates model of jamming suspensions. The work confirms the presence of unsteady behaviour in these suspensions, but only for a narrow range of particle concentrations; and of lower density than previously reported. This highlights some of the weaknesses of the WC model, especially as applied to suspensions of polydisperse anisometric particles such as cornstarch (deviating from monodisperse hard spheres).Cornstarch suspensions are well known for their surprising property of being able to support a person running across the surface of a pool of the suspension — a phenomenon called shear jamming. This phenomenon has far reaching importance across industries involving granular and colloidal solids transport. In this thesis, the design of a novel custom rheometer for the investigation of these suspensions is described. The results of applying the custom rheometer are shown and analysed in the context of the popular Wyart-Cates model of jamming suspensions. The work confirms the presence of unsteady behaviour in these suspensions, but only for a narrow range of particle concentrations; and of lower density than previously reported. This highlights some of the weaknesses of the WC model, especially as applied to suspensions of polydisperse anisometric particles such as cornstarch (deviating from monodisperse hard spheres)
Empirical Bayesian inference on Poisson processes with a Clayton prior distribution
Get PDFDependency between rates of occurrence of events can exist for a variety of reasons.For example, management culture within organisations can have a similar impact on multiple outcomes. Modelling approaches that assume independence between event rates can be mathematically convenient, but they might also fail to account for all the information within the data since the existence of dependency means that data fromone process can provide information about the rate of occurrence on similar processes. However, estimating correlated event rates is challenging.We address this challenge by developing an inference framework to account for such dependency using copulas in order to make full use of available data.We develop an empirical Bayesian inference method based on a multivariate Poisson – Clayton with Gamma marginals probability model. The proposed model aims to capture both aleatory and epistemic uncertainties. We assume that events are generated from a homogeneous Poisson process capturing the pure inherentrandomness in the observations, i.e. the aleatory uncertainty. Epistemic uncertainty is represented by the prior where the marginal distributions of event rates are Gamma, and the underlying correlation is captured by the Clayton copula. Ofparticular interest are situations where we might anticipate low rates of occurrence.The Clayton copula is appropriate for situations with left tail dependence, that is where low rates are considered relatively more correlated compared to high rates.However, estimating copulas dependence parameter using count data can be challenging. Hence, we provide analytical expressions for estimating dependency of the Clayton copula as a function of the count data realised from Poisson processes.We examine the relative accuracy of the model and investigate the robustness of results under different parameter settings. To support comparison between the proposed model and existing theory, we consider the classic empirical Bayes method assuming independent Gamma priors. Findings are based on simulationexperiments. We also evaluate our method when applied for supplier ranking using de - sensitised real data. We explicitly discuss the ranking problem from a Bayesian perspective, and we propose multiple ranking methods. We identify cases with differentfinal rankings which further enhance the importance of not choosing to ignore dependency.Dependency between rates of occurrence of events can exist for a variety of reasons.For example, management culture within organisations can have a similar impact on multiple outcomes. Modelling approaches that assume independence between event rates can be mathematically convenient, but they might also fail to account for all the information within the data since the existence of dependency means that data fromone process can provide information about the rate of occurrence on similar processes. However, estimating correlated event rates is challenging.We address this challenge by developing an inference framework to account for such dependency using copulas in order to make full use of available data.We develop an empirical Bayesian inference method based on a multivariate Poisson – Clayton with Gamma marginals probability model. The proposed model aims to capture both aleatory and epistemic uncertainties. We assume that events are generated from a homogeneous Poisson process capturing the pure inherentrandomness in the observations, i.e. the aleatory uncertainty. Epistemic uncertainty is represented by the prior where the marginal distributions of event rates are Gamma, and the underlying correlation is captured by the Clayton copula. Ofparticular interest are situations where we might anticipate low rates of occurrence.The Clayton copula is appropriate for situations with left tail dependence, that is where low rates are considered relatively more correlated compared to high rates.However, estimating copulas dependence parameter using count data can be challenging. Hence, we provide analytical expressions for estimating dependency of the Clayton copula as a function of the count data realised from Poisson processes.We examine the relative accuracy of the model and investigate the robustness of results under different parameter settings. To support comparison between the proposed model and existing theory, we consider the classic empirical Bayes method assuming independent Gamma priors. Findings are based on simulationexperiments. We also evaluate our method when applied for supplier ranking using de - sensitised real data. We explicitly discuss the ranking problem from a Bayesian perspective, and we propose multiple ranking methods. We identify cases with differentfinal rankings which further enhance the importance of not choosing to ignore dependency
Advanced Materials Processing and Manufacturing ME978 exam papers
No full textAccess restricted to staff and registered students of the University of Strathclyde.PAST EXAM PAPERS ARE NO LONGER BEING ADDED TO STAX. PLEASE VISIT SUPRIMO TO ACCESS AN UP-TO-DATE COLLECTION OF PAST EXAM PAPERS: https://suprimo.lib.strath.ac.uk
Less is more : the neuromorphic engineering advantage
No full textArtificial Neural Networks (ANN) have helped to revolutionise the world of Computer Vision (CV) with modern interpretations of the ANN based on visual cortex creating Convolutional Neural Network (CNN) and the research movement of Deep Learning (DL). Another more biologically inspired movement is that of Neuromorphic Engineering with its spiking neuron model and Spiking Neural Network (SNN). Recently, research has merged large parts of these two research fields allowing Neuromorphic Engineering to gain more momentum, creating a paradigm shift in the approach to CV. This provides the reality of havinga synchronous, low latency and low computational power approach available when utilising the SNN. A novel solution to both semantic segmentation and a framework in which to utilise it is developed. The Perception-Understanding-Action (PUA) framework aims to add a contextual understanding through semantic segmentation, with a low latency and computational SNN, entitled the Spiking Segmentation Network (SpikeSEG). This framework aims to improve the low latency and reactive Perception-Action Cycles used in many constrained robotics tasks. By adding understanding, a low latency approach aims to add no noticeable latency to the system, exploiting the asynchronous advantage that is available when using Neuromorphic Vision Sensors (NVS). The framework allows an end-to-end spiking system to be realised where latency and computational power are limiting factors. Further to semantic segmentation, a novel method for instance segmentation is also proposed with the Hierarchical Unravelling of Linked Kernels with Similarity Matching through Active Spike ashing (HULK-SMASH) algorithm. This solves the difficult problem of unsupervised class instance clustering, deciphering between separate instances of classes on a per sequence and sequence to sequence basis. The algorithm allows each instance within the classification layers to be traced during the decoding back to the pixel space, allowing a pixel-wise instance mapping of each class instance. The algorithm is successfully able to identify the same person within a neuromorphic vision face dataset, while also being able to successfully recover tracking of instance after complete occlusion. Additionally, a novel solution to the non-typical imaging problem, temporal imaging, is presented. This method of 3D imaging makes use of minimal spatial data from a single-point sensor, but with a high-resolution of temporal data captured in a time-of-flight (ToF) manner. To produce images from this method the novel network Spiking-Single Point Imager (Spike-SPI) is required to solve the inverse retrieval problem of creating a 3D depth map from only the temporal data, inferring the spatial locations based on previous temporal sequences it has seen. This network makes us of both encoder-decoder networks and CNNs and their training methods to train the system. These are then converted to an SNN to allow asynchronous, lower latency and lower computational processing. Spike-SPI was able to outperform the current state of the art in 3D depth estimation, losing no accuracy in the CNN to SNN conversion process while gaining the aforementioned benefits.Artificial Neural Networks (ANN) have helped to revolutionise the world of Computer Vision (CV) with modern interpretations of the ANN based on visual cortex creating Convolutional Neural Network (CNN) and the research movement of Deep Learning (DL). Another more biologically inspired movement is that of Neuromorphic Engineering with its spiking neuron model and Spiking Neural Network (SNN). Recently, research has merged large parts of these two research fields allowing Neuromorphic Engineering to gain more momentum, creating a paradigm shift in the approach to CV. This provides the reality of havinga synchronous, low latency and low computational power approach available when utilising the SNN. A novel solution to both semantic segmentation and a framework in which to utilise it is developed. The Perception-Understanding-Action (PUA) framework aims to add a contextual understanding through semantic segmentation, with a low latency and computational SNN, entitled the Spiking Segmentation Network (SpikeSEG). This framework aims to improve the low latency and reactive Perception-Action Cycles used in many constrained robotics tasks. By adding understanding, a low latency approach aims to add no noticeable latency to the system, exploiting the asynchronous advantage that is available when using Neuromorphic Vision Sensors (NVS). The framework allows an end-to-end spiking system to be realised where latency and computational power are limiting factors. Further to semantic segmentation, a novel method for instance segmentation is also proposed with the Hierarchical Unravelling of Linked Kernels with Similarity Matching through Active Spike ashing (HULK-SMASH) algorithm. This solves the difficult problem of unsupervised class instance clustering, deciphering between separate instances of classes on a per sequence and sequence to sequence basis. The algorithm allows each instance within the classification layers to be traced during the decoding back to the pixel space, allowing a pixel-wise instance mapping of each class instance. The algorithm is successfully able to identify the same person within a neuromorphic vision face dataset, while also being able to successfully recover tracking of instance after complete occlusion. Additionally, a novel solution to the non-typical imaging problem, temporal imaging, is presented. This method of 3D imaging makes use of minimal spatial data from a single-point sensor, but with a high-resolution of temporal data captured in a time-of-flight (ToF) manner. To produce images from this method the novel network Spiking-Single Point Imager (Spike-SPI) is required to solve the inverse retrieval problem of creating a 3D depth map from only the temporal data, inferring the spatial locations based on previous temporal sequences it has seen. This network makes us of both encoder-decoder networks and CNNs and their training methods to train the system. These are then converted to an SNN to allow asynchronous, lower latency and lower computational processing. Spike-SPI was able to outperform the current state of the art in 3D depth estimation, losing no accuracy in the CNN to SNN conversion process while gaining the aforementioned benefits
Investigating the experience of well-being in the context of low paid service work in the hospitality and social care sectors
Get PDFThis thesis investigates the experiences and well-being of low paid workers in the hospitality and social care sectors. The study explores how the nature of jobs in these sectors impacts the well-being of employees; the spillover processes that occur between work and home life; and, how the use of HRM practices shape well-being. By conducting case studies of four organisations from these sectors, multiple perspectives were captured through qualitative interviews with senior management and HR practitioners, line managers, and frontline employees. The main contribution is a unique conceptual framework which enabled an exploration of the well-being of low paid workers across the work-family interface. This framework also allowed for the identification and role of underlying management philosophies and HR practices on these experiences to be examined. The analysis demonstrates how employer strategies, in the form of job demands and resources, both directly and indirectly shaped employees' experiences of work and their well-being.This thesis investigates the experiences and well-being of low paid workers in the hospitality and social care sectors. The study explores how the nature of jobs in these sectors impacts the well-being of employees; the spillover processes that occur between work and home life; and, how the use of HRM practices shape well-being. By conducting case studies of four organisations from these sectors, multiple perspectives were captured through qualitative interviews with senior management and HR practitioners, line managers, and frontline employees. The main contribution is a unique conceptual framework which enabled an exploration of the well-being of low paid workers across the work-family interface. This framework also allowed for the identification and role of underlying management philosophies and HR practices on these experiences to be examined. The analysis demonstrates how employer strategies, in the form of job demands and resources, both directly and indirectly shaped employees' experiences of work and their well-being
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