Wolverhampton Intellectual Repository and E-Theses
WIRE is the open access repository for research publications and outputs by researchers based at the University of Wolverhampton.
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Recent Submissions
Item Managing knowledge in the context of smart cities(University of Wolverhampton, 2025-01)In recent years, the concept of smart cities has become more and more popular in scientific literature and international policies. Today, more than half of the world’s population (55%) of the world’s population lives in urban areas, a figure that is expected to rise to 68% by 2050. This exponential urbanisation growth in cities around the world resulted in growing challenges in socioeconomic, environmental and governance domains. Meeting the evolving needs of changing demographic while optimising natural resources utilisation, poses significant challenging to public sector planners and decision-makers. This necessitates a shift into a ‘smart way’ to enable both financially and environmentally sustainable transformation of cities infrastructure without compromising the quality of life of citizens. As public sector organisations try to meet this complex challenge, they need to be innovative. This often calls for the creation, use, capture and exploitation of new knowledge. Within the knowledge economy, the management of knowledge is increasingly considered an important source of sustainable competitive advantage. However, previous studies on smart cities have predominantly focused on technological advancement of a cities hard infrastructure systems, overlooking the managerial dynamics and knowledge management (KM) challenges underlying the development of smart city projects. Effective deployment of knowledge management in smart city contexts remains relatively underexplored in existing literature, indicating a gap in understanding and implementation. Therefore, there is an urgent need for more research and strategic approaches to integrate knowledge management effectively into smart city development processes. Therefore, the aim of this research is to explore the adoption knowledge management in the context of smart cities so as to improve its competitiveness. A mixed-methods approach was adopted to collect and analyse data based on 97 online questionnaire surveys and 15 interviewees from seven organisations involved in smart cities initiatives. The research started with a purposive sampling method that was later adapted to snowball. An online questionnaire survey and semi-structured interview were selected as the data collection tools. SPSS was utilised to analyse the quantitative data gathered from the questionnaire, while content analysis was employed to acquire an in-depth knowledge of the interviews. The study used descriptive statistical analysis and the t-test to compare equality of mean responses between public and private sector organisations. A framework was developed as the output of the research findings. To improve knowledge sharing and to prevent knowledge loss were amongst the main drivers that fuelled the implementation of KM initiatives in the context of smart cities. Lack of senior management trust on the importance of KM principles to support successful implementation of smart cities initiatives is one of the key challenges to adopt KM strategies in the context of smart cities. Whilst, improved worker productivity and increased innovation are among the key benefits of integrating KM in smart cities projects. This study concluded that effective adoption of KM in smart cities initiatives requires a change in the organisational culture to promote knowledge sharing and use. The findings of this research provide valuable insights that can help the public sector organisations decision-makers involved in smart cities projects to implement knowledge management strategies and initiatives to improve their competitiveness.Item Biologically plausible energy-efficient context-sensitive neural networks(University of Wolverhampton, 2025-02)The rapid advancements in artificial intelligence (AI), particularly in deep neural networks (DNNs), have significantly driven the demand for specialised hardware, such as GPUs and TPUs, to meet the growing computational requirements. However, this surge in computational power has led to substantial increases in energy consumption, raising critical concerns about the economic, technical, and environmental sustainability of AI technologies. For instance, training a single large-scale neural network can generate as much carbon dioxide as five automobiles over their lifespans. While notable efforts have been made to enhance the energy efficiency of deep learning (DL) models, the challenge of sustainable computation persists, especially in resource-constrained environments like hearing aid devices. Despite being inspired by the human brain’s functionality, DNNs fall short of matching the brain’s remarkable energy efficiency, where the human brain operates at merely 20 watts while performing complex cognitive tasks. This thesis addresses the imperative need for human-level computational efficiency in DL models through a multidisciplinary approach synthesising AI, neuroscience, and hardware engineering insights. Central to this work is integrating concepts derived from recent neurological discoveries of context-sensitive neurons into DNNs, aiming to enhance energy efficiency in neural network processing. The research introduces a novel context-sensitive (CS) mechanism within a deep convolution network (DNN), demonstrating significant reductions in neural activity and energy consumption compared to state-of-the-art methods. The CS DNN with 18 convolution layers is employed for multimodal (MM) non-linear regression tasks, such as audio-visual speech enhancement (AVSE). It is then compared against a conventional point neuron-inspired DCN in terms of perceptual evaluation of speech (PESQ) and short-time objective intelligibility (STOI). This research shows that the two-point neuron-driven DCN performs comparable to point-neuron DCN. However, the twopoint neuron uses up to 7 times fewer neurons. Furthermore, to demonstrate the scalability of this energy efficiency, the DCN is mapped to a 50-layer convolution network and implemented on Xilinx UltraScale+ MPSoC based Open-MHA (Multimodal hearing aid) platform. The two-point neuron architecture showed 103 times lesser energy (J) consumption for an inference. This research introduces a novel context-sensitive (CS) mechanism within a deep convolutional network (DNN), demonstrating significant reductions in neural activity and energy consumption compared to state-of-the-art methods. The proposed CSDNN, comprising 18 convolutional layers, is employed for multimodal non-linear regression tasks such as audio-visual speech enhancement (AVSE). Its performance is evaluated against a conventional point-neuron-inspired deep convolutional network (DCN) using the Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) metrics. The results indicate that the two-point neuron-driven DCN performs comparably to the point-neuron DCN while utilizing up to seven times fewer neurons. To demonstrate the scalability of this energy-efficient architecture, the DCN is expanded to a 50-layer convolutional network and implemented on the Xilinx UltraScale+ MPSoC-based Open-MHA (Open Multimodal Hearing Aid) platform. The two-point neuron architecture exhibited a 103-fold reduction in energy consumption (J) per inference. Furthermore, this research adopts a two-point neuron-based, biologically plausible training mechanism, which is transformed into a novel multimodal setting and applied to the audio-visual speech enhancement task. Experimental results, compared to a backpropagation-based baseline model, demonstrate outstanding energy efficiency, reducing neuron firing rates by up to 70%. This reduction implies more sustainable implementations, making the approach highly suitable and desirable for embedded systems. Finally, this thesis explores the biological plausibility of the proposed mechanism by implementing context-sensitive (CS) spiking neurons within a spiking neural network (SNN). This implementation provides a comprehensive understanding of the role of two-point neurons both mathematically and empirically. The CS-SNN is applied to a classical non-linear XOR learning task, demonstrating rapid learning—twice as fast as the baseline model—along with improved performance. Building on these contributions, this research contributes to the ongoing pursuit of sustainable and biologically inspired AI by proposing and validating a context-sensitive approach that advances the energy efficiency of deep neural networks (DNNs).Item The negotiation of identities through sport participation of British Muslim females living in the United Kingdom(University of Wolverhampton, 2024-11)This thesis aims to explore the negotiation of identities around sport among British Muslim females living in the United Kingdom. Sports have long been regarded as a significant aspect of culture and identity, providing individuals with opportunities for physical activity, social interaction, and personal growth. However, the experiences of British Muslim females living in the United Kingdom within the realm of sports are unique and often overlooked. This thesis delves into the multifaceted challenges and opportunities faced by British Muslim females in their participation in sports. This research seeks to understand the impact of cultural, religious, and societal expectations on the sports engagement of British Muslim females. It explores how these factors shape their identities and influence their decision-making processes regarding sports participation. Moreover, this research also examines the systemic barriers and discriminatory practices that hinder their access to sports opportunities in the United Kingdom. By examining the experiences and narratives of British Muslim females, this thesis aims to contribute to a better understanding of how identities are negotiated and shaped within the context of sport, specifically among British Muslim females living in the United Kingdom. The study shows that the women felt both included and left out in their sports and Muslim communities. They often felt like outsiders trying to fit into both their world of sports and religious community, leading to different beliefs and behaviours that made it hard for them to fully belong to either group. However, when playing sports that matched Islamic values, it helped bring the Muslim community closer together. So, these athletes experienced feeling welcomed but also excluded at times within their groups. The findings highlight how challenging it can be for Muslim athletes trying to balance their religion with being part of the sporting world, showing a bigger picture of how people struggle with fitting into different areas of life. These observations underscore the intricate interplay between identity formation and senses of belongingness.Item Blockchain with smart contract for reverse shopping of retail industry branded plastic packaging(University of Wolverhampton, 2025-02)Retail industries use an enormous number of plastics, especially packaging. "Reverse Shopping," that is, the returning of branded plastic packaging to the point of purchase (respective retail brands) by deploying blockchain with smart contracts, is our research focus. Employing a local blockchain, implemented in a basic format to store records of collected plastic packaging matched by the smart contract when returned, which also incorporates a reward mechanism courtesy of digital 'coin offerings' (cryptocurrency). To date, both blockchain and smart contracts are operationally standalone in this regard. We are proposing an innovative design model that advocates for (a) Including reverse shopping on retail industries' branded plastic packaging (as opposed to third-party collection points, such as recycling bins). (b) Fostering a 'reverse order' between retailers and manufacturers, like that between retailers and consumers. (c) Aid ethical and transparent sustainability.Item Assessing the societal influence of academic research with ChatGPT: Impact case study evaluations(Wiley, 2025-12-31)Academics and departments are sometimes judged by how their research has benefitted society. For example, the UK’s Research Excellence Framework (REF) assesses Impact Case Studies (ICSs), which are five-page evidence-based claims of societal impacts. This article investigates whether ChatGPT can evaluate societal impact claims and therefore potentially support expert human assessors. For this, various parts of 6,220 public ICSs from REF2021 were fed to ChatGPT 4o-mini along with the REF2021 evaluation guidelines, comparing ChatGPT’s predictions with published departmental average ICS scores. The results suggest that the optimal strategy for high correlations with expert scores is to input the title and summary of an ICS but not the remaining text, and to modify the original REF guidelines to encourage a stricter evaluation. The scores generated by this approach correlated positively with departmental average scores in all 34 Units of Assessment (UoAs), with values between 0.18 (Economics and Econometrics) and 0.56 (Psychology, Psychiatry and Neuroscience). At the departmental level, the corresponding correlations were higher, reaching 0.71 for Sport and Exercise Sciences, Leisure and Tourism. Thus, ChatGPT-based ICS evaluations are simple and viable to support or cross-check expert judgments, although their value varies substantially between fields.
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