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Characterisation of bone loss and development of an electrostimulation intervention to improve bone health in adults with spinal cord injury
Get PDFBone loss following spinal cord injury (SCI) is typically more rapid compared to that associated with aging or postmenopause, leading to high susceptibility to fragility fractures, subsequent health complications and reduced quality of life. Characteristics of this complex form of bone loss have not been fully studied or understood. Furthermore, most of the available rehabilitation approaches are limited by different factors ranging from the low muscle forces that are achievable to safety considerations. Therefore, the aims of this thesis were to develop a more detailedcharacterisation of bone loss in the paralysed limbs following SCI, and to assess the feasibility and effectiveness of a novel approach of Recruiting Antagonistic Muscle Pairs using Electrical Stimulation (RAMP-ES) to maximise the muscle forces produced and its potential to achieve bone stimulation. In this thesis, longitudinal bone loss in the fibula and the regional variation in bone loss across tibia cross-sections over twelve months following SCI were studied for the first time. Furthermore, RAMP-ES protocols were developed and tested in able-bodied participants before assessing the effectiveness of the novel approach on muscle and bone health in people with chronic SCI. Longitudinal loss in BMC and BMD in the fibula was found to be smaller compared to the tibia (-6.9±5.1% and -6.6± 6.0% vs -14.8 ±12.4% and -14.4±12.4%, p=0.02and p=0.03, respectively), in line with previous cross-sectional reports. In the tibia, bone loss was found to vary regionally in the diaphysis, with total loss at four months being a strong predictor of total loss after twelve months postinjury (r=0.84 and r=0.82 for 4% and 66% tibial sites, respectively, both P < 0.001). The novel RAMPES protocol was developed and tested in able-bodied participants and was found tobe practical. A four-month RAMP-ES training schedule improved muscle size (7.3-19.8%) and strength in people with chronic SCI, but it had no clear effect on bone density. These findings contribute a better characterisation of bone loss and showed that the RAMP-ES intervention can improve muscle health following SCI. More studies should be done to further assess or improve the effectiveness of RAMP-ES for bonehealth applications.Bone loss following spinal cord injury (SCI) is typically more rapid compared to that associated with aging or postmenopause, leading to high susceptibility to fragility fractures, subsequent health complications and reduced quality of life. Characteristics of this complex form of bone loss have not been fully studied or understood. Furthermore, most of the available rehabilitation approaches are limited by different factors ranging from the low muscle forces that are achievable to safety considerations. Therefore, the aims of this thesis were to develop a more detailedcharacterisation of bone loss in the paralysed limbs following SCI, and to assess the feasibility and effectiveness of a novel approach of Recruiting Antagonistic Muscle Pairs using Electrical Stimulation (RAMP-ES) to maximise the muscle forces produced and its potential to achieve bone stimulation. In this thesis, longitudinal bone loss in the fibula and the regional variation in bone loss across tibia cross-sections over twelve months following SCI were studied for the first time. Furthermore, RAMP-ES protocols were developed and tested in able-bodied participants before assessing the effectiveness of the novel approach on muscle and bone health in people with chronic SCI. Longitudinal loss in BMC and BMD in the fibula was found to be smaller compared to the tibia (-6.9±5.1% and -6.6± 6.0% vs -14.8 ±12.4% and -14.4±12.4%, p=0.02and p=0.03, respectively), in line with previous cross-sectional reports. In the tibia, bone loss was found to vary regionally in the diaphysis, with total loss at four months being a strong predictor of total loss after twelve months postinjury (r=0.84 and r=0.82 for 4% and 66% tibial sites, respectively, both P < 0.001). The novel RAMPES protocol was developed and tested in able-bodied participants and was found tobe practical. A four-month RAMP-ES training schedule improved muscle size (7.3-19.8%) and strength in people with chronic SCI, but it had no clear effect on bone density. These findings contribute a better characterisation of bone loss and showed that the RAMP-ES intervention can improve muscle health following SCI. More studies should be done to further assess or improve the effectiveness of RAMP-ES for bonehealth applications
Evaluation of a collagen-based hydrogel as a delivery system for Parkinson’s disease therapeutics
No full textParkinson’s disease (PD) is a neurodegenerative disorder that affects the dopaminergic neurons in the substantia nigra. Recently, cell therapy has emerged as a promising therapeutic strategy. To increase the cell viability, biomaterials are used to facilitate the cell deposition through injection. However, the existing delivery approaches have shown limited success in clinical translation. This thesis aims to evaluate a collagen hydrogel as a delivery system for therapeutics for PD. This is achieved with the following objectives. Firstly, the hydrogel usability for delivery to the central nervous system (CNS) was evaluated. A material characterisation was conducted, which showed that the mechanical properties of the hydrogel make it an appropriate system for CNSimplantation. Additionally, the gelation time showed that the hydrogel will form fast enough once injected and will not diffuse to the surrounding tissue. Low swelling ratio was observed, a desirable characteristic for hydrogels delivered to the CNS. However, a high hydrogel mass loss was observed at body temperature and shear rate was shown to have an effect to the mechanical properties, as the hydrogels formed under shear appeared less stiff. This could impact the clinical translation, as injecting the hydrogel could alter its mechanical properties. Secondly, an investigation of the effect of the delivery device design on the flowof collagen during injection was carried out. The effect of the design wasevaluated computationally, and it was shown that as collagen passes through the syringe to the needle, different forces are present, depending on the design. A tapered design with big needle diameter was indicated to be appropriate for collagen delivery. The next objective was the computational assessment of collagen injection to the striatum. The infusion to the brain tissue was evaluated using the biphasic solutemethod. An effect of infusion pressure and the needle tip to the distribution of resulting pressure and stress to the tissue was shown. A linear relationship between the infusion pressure and the resulting pressure and stress was observed, while their relationship with the needle diameter was nonlinear. Finally, the feasibility of a novel biomaterial-based method for the reconstruction of the nigrostriatal pathway was assessed. The feasibility to create a hydrogel tube, long enough to connect the substantia nigra to the striatum was shown.This research has provided further insight to the subject of biomaterial delivery to the CNS and has exhibited that the method of delivery of therapeutics is of pivotal importance for a successful clinical translation.Parkinson’s disease (PD) is a neurodegenerative disorder that affects the dopaminergic neurons in the substantia nigra. Recently, cell therapy has emerged as a promising therapeutic strategy. To increase the cell viability, biomaterials are used to facilitate the cell deposition through injection. However, the existing delivery approaches have shown limited success in clinical translation. This thesis aims to evaluate a collagen hydrogel as a delivery system for therapeutics for PD. This is achieved with the following objectives. Firstly, the hydrogel usability for delivery to the central nervous system (CNS) was evaluated. A material characterisation was conducted, which showed that the mechanical properties of the hydrogel make it an appropriate system for CNSimplantation. Additionally, the gelation time showed that the hydrogel will form fast enough once injected and will not diffuse to the surrounding tissue. Low swelling ratio was observed, a desirable characteristic for hydrogels delivered to the CNS. However, a high hydrogel mass loss was observed at body temperature and shear rate was shown to have an effect to the mechanical properties, as the hydrogels formed under shear appeared less stiff. This could impact the clinical translation, as injecting the hydrogel could alter its mechanical properties. Secondly, an investigation of the effect of the delivery device design on the flowof collagen during injection was carried out. The effect of the design wasevaluated computationally, and it was shown that as collagen passes through the syringe to the needle, different forces are present, depending on the design. A tapered design with big needle diameter was indicated to be appropriate for collagen delivery. The next objective was the computational assessment of collagen injection to the striatum. The infusion to the brain tissue was evaluated using the biphasic solutemethod. An effect of infusion pressure and the needle tip to the distribution of resulting pressure and stress to the tissue was shown. A linear relationship between the infusion pressure and the resulting pressure and stress was observed, while their relationship with the needle diameter was nonlinear. Finally, the feasibility of a novel biomaterial-based method for the reconstruction of the nigrostriatal pathway was assessed. The feasibility to create a hydrogel tube, long enough to connect the substantia nigra to the striatum was shown.This research has provided further insight to the subject of biomaterial delivery to the CNS and has exhibited that the method of delivery of therapeutics is of pivotal importance for a successful clinical translation
Taste for luxury, preference for counterfeits
No full textPreviously held under moratorium from 08/02/2023 until 08/02/2025This thesis explores what is the role of taste in consumption of counterfeit luxury goods and whether engagement in this practice shows the emergence of a new taste regime. The study is
based within Consumer Culture Theory (CCT) with a particular focus on studies that consider consumer identity projects, marketplace cultures and marketplace ideologies, with special
attention to theories of taste, cultural capital, and counterfeit luxury consumption. While the subject of taste has appeared to capture a high level of attention in consumer research, little has been said about how it is exercised in contexts where social acceptability is not given.
This qualitative study therefore intends to close this gap by linking taste to consumption of counterfeit luxury goods and exploring the interplays of this type of consumer behaviour.
This thesis therefore addresses three research questions that facilitate the discussion on the role taste plays for consumers of counterfeit luxury goods. Specifically, the inquiry is structured around establishing how consumers showcase their taste with counterfeit luxury goods; what taste-related practices are performed by these consumers; and what social and cultural conditions allow the formulation of emergent taste regime of counterfeit luxury
goods consumption. To achieve this, the study draws on netnography, phenomenological interviews, wardrobe interviews and visual methods, which work as an eco-system for gaining rich insight into counterfeit taste.
The findings of this study contribute to CCT by establishing how taste is practiced in the less institutionalised contexts. This research contributes to an understanding of “taste as practice” through emergence of three forms of tastes expressed by consumers of both genuine and nongenuine branded goods. Similarly, it deepens the understanding of taste-related practices by proposing that individuals engage in taste curation to reinforce their taste. This study also deepens understanding of how taste is developed outside the context of sensory learning. The final contribution addresses Arsel and Bean (2013) call for the “democratization of
tastemaking through collaborative marketplace communities” by introducing and discussing the concept of “taste communities”. This study concludes by emphasising the importance of further taste investigation in less institutionalised contexts as well as more profound inquiry into legitimization of taste for counterfeit luxury goods.This thesis explores what is the role of taste in consumption of counterfeit luxury goods and whether engagement in this practice shows the emergence of a new taste regime. The study is
based within Consumer Culture Theory (CCT) with a particular focus on studies that consider consumer identity projects, marketplace cultures and marketplace ideologies, with special
attention to theories of taste, cultural capital, and counterfeit luxury consumption. While the subject of taste has appeared to capture a high level of attention in consumer research, little has been said about how it is exercised in contexts where social acceptability is not given.
This qualitative study therefore intends to close this gap by linking taste to consumption of counterfeit luxury goods and exploring the interplays of this type of consumer behaviour.
This thesis therefore addresses three research questions that facilitate the discussion on the role taste plays for consumers of counterfeit luxury goods. Specifically, the inquiry is structured around establishing how consumers showcase their taste with counterfeit luxury goods; what taste-related practices are performed by these consumers; and what social and cultural conditions allow the formulation of emergent taste regime of counterfeit luxury
goods consumption. To achieve this, the study draws on netnography, phenomenological interviews, wardrobe interviews and visual methods, which work as an eco-system for gaining rich insight into counterfeit taste.
The findings of this study contribute to CCT by establishing how taste is practiced in the less institutionalised contexts. This research contributes to an understanding of “taste as practice” through emergence of three forms of tastes expressed by consumers of both genuine and nongenuine branded goods. Similarly, it deepens the understanding of taste-related practices by proposing that individuals engage in taste curation to reinforce their taste. This study also deepens understanding of how taste is developed outside the context of sensory learning. The final contribution addresses Arsel and Bean (2013) call for the “democratization of
tastemaking through collaborative marketplace communities” by introducing and discussing the concept of “taste communities”. This study concludes by emphasising the importance of further taste investigation in less institutionalised contexts as well as more profound inquiry into legitimization of taste for counterfeit luxury goods
Cognitive processes of similarity and combination in conceptual product design engineering
Get PDFTo create new products that satisfy human needs, product design engineers use their technical, manufacturing and creative knowledge to create candidate ideas for new products and develop them into final designs that can be manufactured. All new products have some basis in prior knowledge, and so design can be viewed as a process of knowledge recombination. A variety of methods and tools have been developed to help designers produce novel, useful design concepts through combinational thinking. One way to improve these design aids is by understanding the cognitive processes involved and tailoring methods and tools to foster effective cognitive processing and overcome cognitive constraints. Yet, despite the broad acknowledgement that designers do combine ideas to create new ones, little is known about how designers combine ideas to create new ones. In particular, there is no knowledge about how designers combine design concepts, which are candidate ideas produced earlier in the design process. The research presented in this thesis was conducted to model the cognitive processes involved in design concept combination and design concept similarity judgements. A deductive research approach was used to propose and test two cognitive models. The Dual-Process model of linguistic conceptual combination (Wisniewski, 1997a) was used as a basis for a cognitive model of design concept combination, and the dual-process view of similarity judgements was used as the basis of a model of design concept similarity judgements. Both models involve the same dual processes of comparison and scenario creation, and both models propose that the comparison process involves a process of alignment of structured mental representations. A series of research questions and hypotheses were proposed to test the models and a quasi-experimental research design was developed to evaluate them. The proposed Dual-Process model of design concept similarity judgements was tested in two experiments and it was concluded that student designers make similarity judgements of pairs of early-stage, sketch-based design concepts via a single process of comparison. In the first experiment (n=11), designers were asked to rate the similarity of pairs of design concepts and provide written explanations for their numerical ratings. The responses overwhelmingly indicated that designers make similarity judgements by focusing on the common and different features of the pair, i.e., a comparison process. In a second experiment (n=35), five predictions of the Structural Alignment model of similarity judgements were tested. It was found that similarity can be predicted as a function of the common and different features of a pair of design concepts, consistent with a comparison-based model of similarity judgements. However, only four of the five predictions were supported and so the Structural Alignment model was rejected. This means that it was not possible to draw conclusions about how the comparison process occurs. The proposed Dual-Process model of design concept combination was tested in one experiment (n=30). Student designers combined pairs of early-stage, sketch-based design concepts to create new design concepts that addressed the same brief. The proportion of combination types and their relationship with the similarity of the base concepts were measured and compared with the proposed model. Three kinds of combination were produced : (i) featural, (ii) relational and (iii) ambiguous. As the relative similarity of a pair of design concepts increases, the participants were increasingly likely to produce featural combinations and less likely to produce relational combinations. There was also evidence of a stimulus compatibility effect, a cut-off of relational combinations, and a defaulting to featural combinations. The featural and relational combinations and their relationship with similarity were consistent with the proposed model. However, the combination types were not fully accounted for. Thus, the proposed Dual-Process model does not fully capture the cognitive processes involved in design concept combination.Overall, the initial proposal that both similarity judgments and combination of design concepts occur via the same cognitive processes was incorrect. Comparison is involved in similarity judgements and may plausibly be involved in combination, but while there is evidence of a scenario creation process in design concept combination, there is none for design concept similarity judgements. Additional hypotheses and experiments are proposed to facilitate further research into the cognitive basis of the comparison processes in both models. The research and findings were critiqued to identify the advantages, disadvantages and opportunities and recommendations for future research.To create new products that satisfy human needs, product design engineers use their technical, manufacturing and creative knowledge to create candidate ideas for new products and develop them into final designs that can be manufactured. All new products have some basis in prior knowledge, and so design can be viewed as a process of knowledge recombination. A variety of methods and tools have been developed to help designers produce novel, useful design concepts through combinational thinking. One way to improve these design aids is by understanding the cognitive processes involved and tailoring methods and tools to foster effective cognitive processing and overcome cognitive constraints. Yet, despite the broad acknowledgement that designers do combine ideas to create new ones, little is known about how designers combine ideas to create new ones. In particular, there is no knowledge about how designers combine design concepts, which are candidate ideas produced earlier in the design process. The research presented in this thesis was conducted to model the cognitive processes involved in design concept combination and design concept similarity judgements. A deductive research approach was used to propose and test two cognitive models. The Dual-Process model of linguistic conceptual combination (Wisniewski, 1997a) was used as a basis for a cognitive model of design concept combination, and the dual-process view of similarity judgements was used as the basis of a model of design concept similarity judgements. Both models involve the same dual processes of comparison and scenario creation, and both models propose that the comparison process involves a process of alignment of structured mental representations. A series of research questions and hypotheses were proposed to test the models and a quasi-experimental research design was developed to evaluate them. The proposed Dual-Process model of design concept similarity judgements was tested in two experiments and it was concluded that student designers make similarity judgements of pairs of early-stage, sketch-based design concepts via a single process of comparison. In the first experiment (n=11), designers were asked to rate the similarity of pairs of design concepts and provide written explanations for their numerical ratings. The responses overwhelmingly indicated that designers make similarity judgements by focusing on the common and different features of the pair, i.e., a comparison process. In a second experiment (n=35), five predictions of the Structural Alignment model of similarity judgements were tested. It was found that similarity can be predicted as a function of the common and different features of a pair of design concepts, consistent with a comparison-based model of similarity judgements. However, only four of the five predictions were supported and so the Structural Alignment model was rejected. This means that it was not possible to draw conclusions about how the comparison process occurs. The proposed Dual-Process model of design concept combination was tested in one experiment (n=30). Student designers combined pairs of early-stage, sketch-based design concepts to create new design concepts that addressed the same brief. The proportion of combination types and their relationship with the similarity of the base concepts were measured and compared with the proposed model. Three kinds of combination were produced : (i) featural, (ii) relational and (iii) ambiguous. As the relative similarity of a pair of design concepts increases, the participants were increasingly likely to produce featural combinations and less likely to produce relational combinations. There was also evidence of a stimulus compatibility effect, a cut-off of relational combinations, and a defaulting to featural combinations. The featural and relational combinations and their relationship with similarity were consistent with the proposed model. However, the combination types were not fully accounted for. Thus, the proposed Dual-Process model does not fully capture the cognitive processes involved in design concept combination.Overall, the initial proposal that both similarity judgments and combination of design concepts occur via the same cognitive processes was incorrect. Comparison is involved in similarity judgements and may plausibly be involved in combination, but while there is evidence of a scenario creation process in design concept combination, there is none for design concept similarity judgements. Additional hypotheses and experiments are proposed to facilitate further research into the cognitive basis of the comparison processes in both models. The research and findings were critiqued to identify the advantages, disadvantages and opportunities and recommendations for future research
Improving understanding of acute recurrent tonsillitis through 3D bioprinting of bacterial biofilms
No full textIntroduction: Acute recurrent tonsillitis is a chronic, biofilm-related infection which is a significant burden to patients and the NHS. It is often treated with repeated courses of antibiotics, which contributes to antimicrobial resistance. Studying surface associated biofilms is key to understanding this disease process. In vitromodelling of this type of infection using 3D bioprinted hydrogels is a promising approach to achieve this. The aim of this study was to create the first 3D in vitro model of a multi-strain bacterial biofilm, with assessment by morphotype and viability analysis, and antibiotic susceptibility testing. Methodology: This study required development of a hydrogel formulation which displayed mechanical properties suitable for 3D printing and supported bacterial growth and formation of biofilm. Multi-strain bacterial culture of pseudomonas fluorescens and escherichia coli K-12 was performed to allow production of bacterial bioink and bioprinting of bacteria-laden 3D hydrogel construct fabricated using computer-aided design. This construct was cultured to develop a biofilm. The resulting specimens were assessed by morphotype and viability analysis by fluorescence microscopy, and antibiotic sensitivity testing versus a planktonic culture control. Results: A 3D printed bacteria-laden hydrogel construct was successfully fabricated. Biofilms were observed usingoptical fluorescence microscopy. A Live/Dead cellular staining protocol demonstrated biofilm viability, with high ‘Live’ signal in visualised biofilm colonies. Antibiotic sensitivity testing was inconclusive. Discussion: This study demonstrates first use of 405 nm light-based stereolithography 3D printing of a bacteria-ladenbioink to manufacture a hydrogel construct which supports formation of bacterial biofilms. Further development of this biofilm model will increase fidelity and improve understanding of acute recurrent tonsillitis. This could support development of novel therapeutics which will in turn will reduce excessive antibiotic prescribing which drives antimicrobial resistance. Initiating a study with clinically relevant ex vivo tonsil bacteria will be an important next step in improving treatment of this prodigious, impactful, but understudied disease.Introduction: Acute recurrent tonsillitis is a chronic, biofilm-related infection which is a significant burden to patients and the NHS. It is often treated with repeated courses of antibiotics, which contributes to antimicrobial resistance. Studying surface associated biofilms is key to understanding this disease process. In vitromodelling of this type of infection using 3D bioprinted hydrogels is a promising approach to achieve this. The aim of this study was to create the first 3D in vitro model of a multi-strain bacterial biofilm, with assessment by morphotype and viability analysis, and antibiotic susceptibility testing. Methodology: This study required development of a hydrogel formulation which displayed mechanical properties suitable for 3D printing and supported bacterial growth and formation of biofilm. Multi-strain bacterial culture of pseudomonas fluorescens and escherichia coli K-12 was performed to allow production of bacterial bioink and bioprinting of bacteria-laden 3D hydrogel construct fabricated using computer-aided design. This construct was cultured to develop a biofilm. The resulting specimens were assessed by morphotype and viability analysis by fluorescence microscopy, and antibiotic sensitivity testing versus a planktonic culture control. Results: A 3D printed bacteria-laden hydrogel construct was successfully fabricated. Biofilms were observed usingoptical fluorescence microscopy. A Live/Dead cellular staining protocol demonstrated biofilm viability, with high ‘Live’ signal in visualised biofilm colonies. Antibiotic sensitivity testing was inconclusive. Discussion: This study demonstrates first use of 405 nm light-based stereolithography 3D printing of a bacteria-ladenbioink to manufacture a hydrogel construct which supports formation of bacterial biofilms. Further development of this biofilm model will increase fidelity and improve understanding of acute recurrent tonsillitis. This could support development of novel therapeutics which will in turn will reduce excessive antibiotic prescribing which drives antimicrobial resistance. Initiating a study with clinically relevant ex vivo tonsil bacteria will be an important next step in improving treatment of this prodigious, impactful, but understudied disease
Enhancing remanufacturing automation using deep learning approach
Get PDFIn recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces
Design and implementation of real-time cognitive dynamic spectrum radio, targeting the FM radio band with PHYDYAS FS-FBMC
Get PDFDemand for wireless connectivity is exponentially increasing. Allocated bands in the Radio Frequency (RF) spectrum are commonly presented as being nearly at capacity but in reality, they are often under-utilised. New shared spectrum regulations, combined with Dynamic Spectrum Access (DSA) technologies and Software Defined Radio (SRD) allow third parties to access vacant spectrum that has been traditionally licensed to broadcasters and mobile network operators. Regulators and research institutions worldwide are actively exploring the sharing of finite spectral resources, driving a wireless revolution that will bring lower cost and ubiquitous connectivity.;This thesis presents and validates a disruptive new spectrum sharing technique that facilitates access to the significant amount of vacant spectrum in the band traditionally used for analogue FM Radio broadcasting (88-108 MHz), providing a potential communications solution for load balancing and demand side management in smart grid networks. In this work, a novel, real-time DSA-enabled radio transmitter is designed, implemented, and targeted to programmable 'ZynqSDR' hardware, and investigations are carried out to determine whether it is capable of coexisting with incumbent FM Radio stations. The transmitter uses the Frequency Spread Filter Bank Multicarrier (FS-FBMC) modulation scheme - which has low levels of Out-Of-Band (OOB) leakage - and a non-contiguous subchannel mask, which can automatically reconfigure itself in real time to change the spectral characteristics of the output signal. It was developed using low level Digital Signal Processing (DSP) components from within MATLAB and Simulink.;The FBMC Secondary User (SU) radio was shown to cause minimal interference to FM Radio stations when 'transmitting' at low broadcast powers (e.g. 20 dBm) and using a 200 kHz guardband, indicating that an SU such as the one proposed in this thesis would be capable of legally coexisting with (and transmit alongside) incumbent FM Radio signals; provided radio spectrum regulations were modified to permit legal operation.Demand for wireless connectivity is exponentially increasing. Allocated bands in the Radio Frequency (RF) spectrum are commonly presented as being nearly at capacity but in reality, they are often under-utilised. New shared spectrum regulations, combined with Dynamic Spectrum Access (DSA) technologies and Software Defined Radio (SRD) allow third parties to access vacant spectrum that has been traditionally licensed to broadcasters and mobile network operators. Regulators and research institutions worldwide are actively exploring the sharing of finite spectral resources, driving a wireless revolution that will bring lower cost and ubiquitous connectivity.;This thesis presents and validates a disruptive new spectrum sharing technique that facilitates access to the significant amount of vacant spectrum in the band traditionally used for analogue FM Radio broadcasting (88-108 MHz), providing a potential communications solution for load balancing and demand side management in smart grid networks. In this work, a novel, real-time DSA-enabled radio transmitter is designed, implemented, and targeted to programmable 'ZynqSDR' hardware, and investigations are carried out to determine whether it is capable of coexisting with incumbent FM Radio stations. The transmitter uses the Frequency Spread Filter Bank Multicarrier (FS-FBMC) modulation scheme - which has low levels of Out-Of-Band (OOB) leakage - and a non-contiguous subchannel mask, which can automatically reconfigure itself in real time to change the spectral characteristics of the output signal. It was developed using low level Digital Signal Processing (DSP) components from within MATLAB and Simulink.;The FBMC Secondary User (SU) radio was shown to cause minimal interference to FM Radio stations when 'transmitting' at low broadcast powers (e.g. 20 dBm) and using a 200 kHz guardband, indicating that an SU such as the one proposed in this thesis would be capable of legally coexisting with (and transmit alongside) incumbent FM Radio signals; provided radio spectrum regulations were modified to permit legal operation
Classification of defects for non-destructive inspection using contact sensors and data analysis
Get PDFThe requirement for Non Destructive Testing of composite structures is paramount in Aerospace to maintain structural integrity. There are several established Non Destructive Testing and Inspection methods available dependant on the individual requirements of the structure and situation. Most established non contact methods require an uninterrupted line of sight to the structure to produce an accurate report of the underlying condition. Methods using contact sensors, such as ultrasound, require some form of surface preparation or a couplant to provide accurate readings. Within the Maintenance Repair and Overhaul (MRO) environment, such requirements results in the removal of a component from the maintenance and repair processes, as well as surface preparation to conduct an inspection. This additional time results in a detrimental impact in regards to both financial costs and the time taken to conduct the full maintenance and repair processes. This thesis will focus on the development and application of a tooling system utilising contact temperature sensors which is compatible with the MRO environment, requires minimal surface preparation and is unaffected by line of sight issues created due to the repair process (such as bagging films and breather materials). Once thermal data has been captured, data analysis should be conducted in order to indicate areas of interest (e.g. delaminations and disbonds) as well as to infer useful material properties (i.e. fibre orientation).;The indications will be presented in such a manner visually, so as to require minimal training for operators to adopt. Representative composite structures are thermally profiled within simulations and experimentally, to produce library data of expected ther- mal responses as well as for use in training Machine Learning algorithms for classification of defects. The methods developed are then tested using data previously unseen by trained algorithms or within the library data, to score their performance. The thesis presents a two part tool capable of heating a composite sample and record the resultant thermal response, without impact from the vacuum bagging process or a requirement for special surface preparation. It is shown to successfully present impact damage within composite sandwich structures in an areal form familiar to existing inspectors. The tooling in conjunction with an experimentally captured Mean Temperature profile library successfully indicates fibre orientation of biaxial 5 harness satin weave carbon fibre reinforced polymer laminates common within aerospace. This method is performs with an overall accuracy of 87-93% on samples with artificially introduced Gaussian noise. The analysis of transient thermal conductivity profile within a sample is demonstrated to successfully indicate delaminations of maximum acceptable tolerances within composite structures. This method utilises machine learning algorithms in the form of Support Vector Machine (SVM), and Random Forest (RF), achieving overall accuracy of 90% with SVM. Existing methods of tap testing produce accuracy between 73-81%. The main contributions of this Thesis can be summarised as: the design and creation of a versatile contact-based thermography tool that can be used for a variety of NDI tasks; the development of a contact-based thermography technique that utilises contact temperature sensors to assess impact damage; the creation and validation of a mean temperature response library capable of identifying the fibre orientation within a composite laminate panel based on its thermal response; the development of a contact temperature sensor based thermography method to indicate delaminations within a composite laminate utilising step heating and machine learningThe requirement for Non Destructive Testing of composite structures is paramount in Aerospace to maintain structural integrity. There are several established Non Destructive Testing and Inspection methods available dependant on the individual requirements of the structure and situation. Most established non contact methods require an uninterrupted line of sight to the structure to produce an accurate report of the underlying condition. Methods using contact sensors, such as ultrasound, require some form of surface preparation or a couplant to provide accurate readings. Within the Maintenance Repair and Overhaul (MRO) environment, such requirements results in the removal of a component from the maintenance and repair processes, as well as surface preparation to conduct an inspection. This additional time results in a detrimental impact in regards to both financial costs and the time taken to conduct the full maintenance and repair processes. This thesis will focus on the development and application of a tooling system utilising contact temperature sensors which is compatible with the MRO environment, requires minimal surface preparation and is unaffected by line of sight issues created due to the repair process (such as bagging films and breather materials). Once thermal data has been captured, data analysis should be conducted in order to indicate areas of interest (e.g. delaminations and disbonds) as well as to infer useful material properties (i.e. fibre orientation).;The indications will be presented in such a manner visually, so as to require minimal training for operators to adopt. Representative composite structures are thermally profiled within simulations and experimentally, to produce library data of expected ther- mal responses as well as for use in training Machine Learning algorithms for classification of defects. The methods developed are then tested using data previously unseen by trained algorithms or within the library data, to score their performance. The thesis presents a two part tool capable of heating a composite sample and record the resultant thermal response, without impact from the vacuum bagging process or a requirement for special surface preparation. It is shown to successfully present impact damage within composite sandwich structures in an areal form familiar to existing inspectors. The tooling in conjunction with an experimentally captured Mean Temperature profile library successfully indicates fibre orientation of biaxial 5 harness satin weave carbon fibre reinforced polymer laminates common within aerospace. This method is performs with an overall accuracy of 87-93% on samples with artificially introduced Gaussian noise. The analysis of transient thermal conductivity profile within a sample is demonstrated to successfully indicate delaminations of maximum acceptable tolerances within composite structures. This method utilises machine learning algorithms in the form of Support Vector Machine (SVM), and Random Forest (RF), achieving overall accuracy of 90% with SVM. Existing methods of tap testing produce accuracy between 73-81%. The main contributions of this Thesis can be summarised as: the design and creation of a versatile contact-based thermography tool that can be used for a variety of NDI tasks; the development of a contact-based thermography technique that utilises contact temperature sensors to assess impact damage; the creation and validation of a mean temperature response library capable of identifying the fibre orientation within a composite laminate panel based on its thermal response; the development of a contact temperature sensor based thermography method to indicate delaminations within a composite laminate utilising step heating and machine learnin
Road network recovery from concurrent capacity-reducing incidents : model development and optimisation
Get PDFLocal and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions.Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions
Compressive mechanical behaviour of nitinol wires used in aortic stent grafts
No full textThe Anaconda endovascular stent graft is a medical device designed to treat abdominal aortic aneurysms, composed of a nitinol wire structure and a fabric graft. The graft is required to undergo significant deformations before and after the implementation of the device. This thesis addresses the material characterization of thin nitinol wire taking advantage of the test methods already established for tensile and compressive behaviour, mainly focussing on the compressive response. The tensile behaviour here presented consists of a preliminary study of the localised deformation on nitinol wire. The mechanical characterization of the wire under compression starts with the implementation of digital image correlation (DIC) technique to measure the sample strainfield. The compressive test method was found to be unsuitable for compressive loading, where it was not possible to replicate the compressive tests, validate the DIC technique and does not hold the sample securely during the compressive test. This finding led to a complete change of the research goals and paved the way for the development of a test method for fine nitinol wire.A compressive test method is therefore proposed, which is shown to be valid under compressive loading and for obtaining the compressive material parameters as input to the numerical models. A parametric study was undertaken to understand the optimum ratio between the length and diameter of the sample. An attempt at using this method in a temperature-controlled environment is also presented. The application of the Auricchio constitutive model, implemented in the finite element software Abaqus, is considered. A comparison with an alternative non-commercial modelis also studied and reported along with some suggested improvements. This results in better prediction of the asymmetric behaviour of nitinol wire under compressive and tensile loading and is shown to be very promising in physically representing the bending behaviour of nitinol wire.The Anaconda endovascular stent graft is a medical device designed to treat abdominal aortic aneurysms, composed of a nitinol wire structure and a fabric graft. The graft is required to undergo significant deformations before and after the implementation of the device. This thesis addresses the material characterization of thin nitinol wire taking advantage of the test methods already established for tensile and compressive behaviour, mainly focussing on the compressive response. The tensile behaviour here presented consists of a preliminary study of the localised deformation on nitinol wire. The mechanical characterization of the wire under compression starts with the implementation of digital image correlation (DIC) technique to measure the sample strainfield. The compressive test method was found to be unsuitable for compressive loading, where it was not possible to replicate the compressive tests, validate the DIC technique and does not hold the sample securely during the compressive test. This finding led to a complete change of the research goals and paved the way for the development of a test method for fine nitinol wire.A compressive test method is therefore proposed, which is shown to be valid under compressive loading and for obtaining the compressive material parameters as input to the numerical models. A parametric study was undertaken to understand the optimum ratio between the length and diameter of the sample. An attempt at using this method in a temperature-controlled environment is also presented. The application of the Auricchio constitutive model, implemented in the finite element software Abaqus, is considered. A comparison with an alternative non-commercial modelis also studied and reported along with some suggested improvements. This results in better prediction of the asymmetric behaviour of nitinol wire under compressive and tensile loading and is shown to be very promising in physically representing the bending behaviour of nitinol wire
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