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Integrated cooperative SWIPT THz-NOMA and secure DLTs for efficient 6G communications
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis concentrated on advancing the performance of 6G communications and networks, evaluating efficiency, reliability, resource management, cost-effectiveness, energy consumption, user fairness, and security. Key enabling technologies and intelligent techniques, including terahertz (THz) frequency bands, nonorthogonal multiple access (NOMA), energy harvesting, cooperative networking (decode-and-forward relaying), and artificial intelligence were explored to achieve the targeted objectives. Cooperative simultaneous wireless information and power transfer (SWIPT) in THz-NOMA (and hybrid-NOMA) were integrated to overcome challenges in THz communications. Besides, the proposed deep learning-based 6G channel estimation (CE) model established a robust system against intelligent attacks associated with intelligent systems.
The research highlighted the significant improvements achieved by these technologies, showcasing a 70% enhancement in wireless transmission performance for 6G communications compared to existing systems. The proposed cooperative SWIPT THz-NOMA system demonstrated noteworthy improvements in energy efficiency (EE), spectral efficiency (SE), and other critical metrics. Utilizing the proposed technologies resulted in a remarkable improvement over conventional cooperative networking.
Additionally, the investigation extended to exploring cooperative SWIPT THz multiple-input multiple-output (MIMO) NOMA, introducing path-selection mechanisms and reliable transmission strategies. That outperformed the basic THz-NOMA and provided more enhancement in terms of SE and EE. The entire study emphasized a simpler design, reduced transceiver hardware (which accordingly reduced energy consumption, complexity, and cost), and improved reliability compared to previous work.
Furthermore, this work addressed the imperfections in successive interference cancellation (SIC) in 6G NOMA-based communications, proposing an optimized two-user pairing scheme with SWIPT and cooperative hybrid-NOMA (H-NOMA) in THz communications. It presented a system performance improvement of 75% in SE and EE compared to conventional NOMA and orthogonal multiple access techniques.
Moreover, the work focused on upgrading 5G communication systems to be 6G-compatible and meet the 6G stringent requirements of emerging technologies/applications. It addressed challenges in THz transmission, improving wireless connectivity, resource availability, processing, robustness, and capacity. It evaluated the best pairing strategy in H-NOMA, investigating all the possible SWIPT pairs with the available line-of-sight users to optimize the best pair/performance.
Finally, the last part addressed CE vulnerability to adversarial attacks in 6G systems. The proposed deep autoencoder-based 6G CE model demonstrated robustness against adversarial attacks, providing a promising solution for securing 6G networks. System security and accuracy were validated through simulations, presenting an added value to the field
Advancing Electric Vehicle Technologies: A Comparative Analysis of Lithium-Ion and Emerging Solid-State Battery Systems
Get PDFThis paper examines the evolving battery technology landscape within the electric vehicle (EV) industry, with a particular focus on the shift towards solid-state batteries as a promising alternative to the dominant lithium-ion technologies. While lithium-ion batteries currently lead the market, their inherent drawbacks such as flammability, dendrite growth, and thermal instability are significant impediments to the rapid adoption of EV technologies. Solid-state batteries are highlighted as a viable and superior alternative due to their enhanced energy density and safety profiles. The paper also explores modelling techniques instrumental in enhancing the performance and longevity of lithium-ion and solid-state solutions, potentially accelerating the shift towards EVs in pursuit of global net-zero objectives.
Improving IIoT performance through dynamic configuration of edge computing/MWSN routing protocol
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the rapid growth of the industrial Internet of things (IIoT) and the advent of fifth
generation (5G) technology have made efficient data communication and processing increasingly
crucial. Several requirements concerning IIoT and 5G like connectivity, scalability,
energy efficiency, interoperability, security, and privacy, are crucial for modern industrial
settings with numerous connected devices generating large volumes of data. This
study presents two novel techniques to address these requirements in mobile wireless sensor
networks (MWSNs), a key component of IIoT. These techniques are designed to handle
the dynamic nature of mobile sensor nodes (MSNs) while considering resource constraints.
The development features novel protocols designed to enhance the network lifetime in
MWSNs, thereby improving the quality of service (QoS) for the entire network through
two significant contributions.
Firstly, a novel dual tier cluster-based routing protocol, DTC-BR, organizes the network
into virtual zones. Each zone includes multiple cluster members (CMs) that collect data
and a single cluster head (CH) that aggregates it. DTC-BR was evaluated in MATLAB
against metrics such as energy consumption, network lifetime, and scalability. The efficiency
of DTC-BR is highlighted in comparative results, showing a network lifetime increase
of 6%, 21%, 25%, and 37% over dynamic directional routing (DDR), mobilityaware
centralized clustering algorithm (MCCA), low energy adaptive clustering hierarchymobile
energy efficient and connected (LEACH-MEEC), and low energy adaptive clustering
hierarchy-mobile (LEACH-Mobile or LEACH-M) protocols respectively, particularly
exhibiting efficiency in larger networks with a large number of sensor nodes (SNs).
Secondly, a dynamic resource allocation based on real-time elastic approach, DRAREA,
has been developed. This technique integrates DTC-BR and involves MSNs offloading
data to CHs, which then transmit it directly to nearby edge servers (ESs). The offloading
to ESs enhances computing efficiency, prolongs battery life, and optimizes energy use.
MATLAB simulations have demonstrated the superiority of DRA-REA in task offloading
and resource utilization, significantly improving network QoS and mobile IIoT devices
energy conservation. It outperformed benchmarks like genetic algorithm based multi-edge collaborative computation offloading (GECO) and resource-agnostic microservice offloading
(RAISE) in terms of queue size by more than 55% and execution latency by more than
22%.
These techniques offer viable solutions to IIoT challenges like response time, battery
life, bandwidth, and privacy. They effectively utilize resources and balance workloads
across ESs, addressing the critical demands of IIoT applications and enhancing the overall
performance of MWSNs. As the integration between clustering-based routing protocol and
incorporating edge computing (EC) in resource management is currently in an exploratory
stage, there is a significant lack of research in this area. Therefore, the potential of the
results holds significant promise for driving advancements in the field of IIoT. The findings
of this research provide a solid foundation for further exploration and innovation in resource
utilization and system performance optimization within the IIoT domain
Understanding the Psychological, Relational, Sociocultural, and Demographic Predictors of Loneliness Using Explainable Machine Learning
Get PDFBrief report. Complete research materials, datasets, and data analysis scripts are available at https://osf.io/9mvbk/?view_only=6497e5306e9e47bdbe270a7f82fd1d71 .Supplemental material is available online at: :https://psycnet.apa.org/doi/10.1037/sah0000594.supp .Loneliness—an important indicator of social health—is increasingly recognized to derive from factors operating at multiple levels. However, simultaneously examining the role of factors at multiple levels implies using large samples and testing multiple factors at the same time, which traditional statistical methods cannot accommodate. We used machine learning techniques to address this problem. We identify the most important out of 32 correlates of loneliness frequency in a large sample of people ages 16+ years, residing all over the world, who took part in the British Broadcasting Corporation Loneliness Experiment. Factors spanned individual, relational, sociocultural, and demographical areas. The most statistically important associate of loneliness was daily experiences with prejudice (or stigma), followed by couple satisfaction, neuroticism (emotional stability), personal self-esteem, average hours spent alone daily, extraversion, social capital, and relational mobility. Interaction effects were also evident, showing that experiences with prejudice were most negatively associated with loneliness when individuals spent a lot of time alone and the least when individuals were emotionally stable, had high personal self-esteem, or had high levels of couple satisfaction. This research highlights what factors need to be considered when developing effective interventions to mitigate loneliness.The data collection was funded by the Wellcome Trust (Grant 209625/Z/17/ Z, awarded to Pamela Qualter, Manuela Barreto, and Christina Victor). Open Access funding provided by University of Exete
Re-imagining Business School Doctoral Programmes: Enhancing Impact, Aligning with Industry, and Developing the Next Generation
Get PDFAt head of pages: EFMD Global Focus_Iss.1 Vol.18.Originally published as Nicola Palmer, Julie Davies and Clare Viney, 'Re-imagining Business School Doctoral Programmes: Enhancing Impact, Aligning with Industry, and Developing the Next Generation' Global Focus: The EFMD business magazine, 29 January 2024 (https://globalfocusmagazine.com/issue_info/vol1801-24/).Nicola Palmer, Julie Davies and Clare Viney discuss how doctoral research programmes should be more dynamic and tailored to better prepare the next generation of business school faculty members.OA Funder: EFMD
The effect of bipolar bihemispheric tDCS on executive function and working memory abilities
No full textData availability statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Introduction: Cognitive functioning is central to the ability to learn, problem solve, remember, and use information in a rapid and accurate manner and cognitive abilities are fundamental for communication, autonomy, and quality of life. Transcranial electric stimulation (tES) is a very promising tool shown to improve various motor and cognitive functions. When applied as a direct current stimulus (transcranial direct current stimulation; tDCS) over the dorsolateral pre-frontal cortex (DLPFC), this form of neurostimulation has mixed results regarding its ability to slow cognitive deterioration and potentially enhance cognitive functioning, requiring further investigation. This study set out to comprehensively investigate the effect that anodal and cathodal bipolar bihemispheric tDCS have on executive function and working memory abilities.
Methods: 72 healthy young adults were recruited, and each participant was randomly allocated to either a control group (CON), a placebo group (SHAM) or one of two neurostimulation groups (Anodal; A-STIM and Cathodal; C-STIM). All participants undertook cognitive tests (Stroop & N Back) before and after a 30-minute stimulation/ sham/ control protocol.
Results: Overall, our results add further evidence that tDCS may not be as efficacious for enhancing cognitive functioning as it has been shown to be for enhancing motor learning when applied over M1. We also provide evidence that the effect of neurostimulation on cognitive functioning may be moderated by sex, with males demonstrating a benefit from both anodal and cathodal stimulation when considering performance on simple attention trial types within the Stroop task.
Discussion: Considering this finding, we propose a new avenue for tDCS research, that the potential that sex may moderate the efficacy of neurostimulation on cognitive functioning.The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article
Comorbid health conditions and their impact on social isolation, loneliness, quality of life, and well-being in people with dementia: longitudinal findings from the IDEAL programme
Get PDFAvailability of data and materials:
IDEAL data were deposited with the UK Data Archive in April 2020. Details of how to access the data can be found here: https://reshare.ukdataservice.ac.uk/854293/.Supplementary Information is available online at: https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-023-04601-x#Sec13 .Copyright . Background:
Most people with dementia have multiple health conditions. This study explores (1) number and type of health condition(s) in people with dementia overall and in relation to age, sex, dementia type, and cognition; (2) change in number of health conditions over two years; and (3) whether over time the number of health conditions at baseline is related to social isolation, loneliness, quality of life, and/or well-being.
Methods:
Longitudinal data from the IDEAL (Improving the experience of Dementia and Enhancing Active Life) cohort were used. Participants comprised people with dementia (n = 1490) living in the community (at baseline) in Great Britain. Health conditions using the Charlson Comorbidity Index, cognition, social isolation, loneliness, quality of life, and well-being were assessed over two years. Mixed effects modelling was used.
Results:
On average participants had 1.8 health conditions at baseline, excluding dementia; increasing to 2.5 conditions over two years. Those with vascular dementia or mixed (Alzheimer’s and vascular) dementia had more health conditions than those with Alzheimer’s disease. People aged ≥ 80 had more health conditions than those aged < 65 years. At baseline having more health conditions was associated with increased loneliness, poorer quality of life, and poorer well-being, but was either minimally or not associated with cognition, sex, and social isolation. Number of health conditions had either minimal or no influence on these variables over time.
Conclusions:
People with dementia in IDEAL generally had multiple health conditions and those with more health conditions were lonelier, had poorer quality of life, and poorer well-being.‘Improving the experience of Dementia and Enhancing Active Life: living well with dementia. The IDEAL study’ was funded jointly by the Economic and Social Research Council (ESRC) and the National Institute for Health and Care Research (NIHR) through grant ES/L001853/2. Investigators: L. Clare, I.R. Jones, C. Victor, J.V. Hindle, R.W. Jones, M. Knapp, M. Kopelman, R. Litherland, A. Martyr, F.E. Matthews, R.G. Morris, S.M. Nelis, J.A. Pickett, C. Quinn, J. Rusted, J. Thom. ESRC is part of UK Research and Innovation (UKRI). ‘Improving the experience of Dementia and Enhancing Active Life: a longitudinal perspective on living well with dementia. The IDEAL-2 study’ is funded by Alzheimer’s Society, grant number 348, AS-PR2-16-001. Investigators: L. Clare, I.R. Jones, C. Victor, C. Ballard, A. Hillman, J.V. Hindle, J. Hughes, R.W. Jones, M. Knapp, R. Litherland, A. Martyr, F.E. Matthews, R.G. Morris, S.M. Nelis, C. Quinn, J. Rusted. S. Sabatini acknowledges the support of the Economic and Social Research Council (ES/X007766/1). This report is independent research supported by the National Institute for Health and Care Research Applied Research Collaboration South-West Peninsula. The views expressed in this publication are those of the authors and not necessarily those of the ESRC, UKRI, NIHR, the Department of Health and Social Care, the National Health Service, or Alzheimer’s Society. The support of ESRC, NIHR and Alzheimer’s Society is gratefully acknowledged. L. Clare and L. Allan acknowledge support from the NIHR Applied Research Collaboration South-West Peninsula. L. Allan additionally acknowledges support from the NIHR Exeter Biomedical Research Centre (BRC)
When Russia and Israel talk about setting up 'buffer zones' what they are really talking about is a land grab
Get PDFPerspective article.In the conflicts raging in Ukraine and the Middle East, we have recently seen calls for the establishment of what are being referred to as “buffer zones”.
Russia has proposed setting one up around Ukraine’s second city, Kharkiv in the north-east of the country. This, the Kremlin claims, is to protect Russian towns from shelling and missile attacks from Ukrainian territory.
Israel, meanwhile, wants to establish a buffer zone in southern Lebanon. It says it needs to protect nearly 70,000 civilians returning to their homes, which they have abandoned in the past year after rocket attacks by Hezbollah.Brunel University London provides funding as a member of The Conversation UK
Nontechnical and technical artificial intelligence capability
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonArtificial intelligence (AI) has attracted huge attention in management field. Its application in organizations has become a common phenomenon. Academics are actively studying how to promote firms to use AI techniques more effectively and the impact of this phenomenon. Especially based on the resource-based view (RBV), scholars have investigated the resources and capabilities that are helpful for firms to apply AI, and developed relevant concepts such as AI capability (AIC). However, there are still many issues that have not been studied, for example what factors can facilitate the improvement of AIC, what factors affect the relationship between AIC and organizational performance, etc. In order to fill these research gaps, this study proposes organizational and contextual antecedents that may influence the development of firm AIC based on RBV and institutional theory. Through the review of AI research, the concepts of nontechnical AIC (NAIC) and technical AIC (TAIC) are constructed from the perceived divergence of nontechnical and technical research. It also proposes corresponding conceptual model and empirically tests the relationships between NAIC and TAIC and different antecedents, as well as how they ultimately affect firm performance. The data was collected from 206 firms in the Yangtze River Delta region of China that have used AI techniques for more than a year. SPSS is used to perform structural equation model analysis and test hypotheses. Data analysis results show that exploitation strategy, coercive pressure, and mimetic pressure can improve firm NAIC. Exploration, leaders’ AI knowledge, and mimetic pressure will improve firm TAIC, and these relationships are moderated to varying degrees by the firm’s data-driven culture. NAIC and TAIC both have a very significant positive impact on firm performance, and they are also moderated by firm international presence. These findings confirm the feasibility of understanding and studying AIC from the perspective of technical relevance, make theoretical contributions to AI-related research, and provide suggestions for management practices of firms applying AI techniques
An experimental study on evaporation, puffing, micro-explosion, and secondary breakup of multi-fuel blend droplet
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonTo mitigate the significant impact of CO₂ and other GHG on global warming and climate change, it is crucial to target one of the major sources of these emissions—fossil fuel-powered vehicles. These vehicles include cars, trucks, and buses, relying on internal combustion engines and are significant contributors to CO₂ emissions within the transportation sector. Although these vehicles are gradually being replaced by renewable energy and hybrid alternatives, they still hold a significant share of the market and vehicle fleet, making it essential to improve their efficiency and reduce emissions during this transition. Supported by advanced strategies, such as injection techniques—CRDI, PI and VIT, combustion technologies—HCCI, PCCI, LTC and DF, after treatment systems—SCR, DPF and EGR, conventional diesel fuel continues to be widely utilized due to its high energy density and superior fuel efficiency with ICEs. Blending diesel with alternative biofuels such as biodiesel and bio-alcohols, presents a promising approach for enhancing engine performance and reducing emissions. The study critically examines the potential of these alternative biofuels usability in ICEs, to enhance fuel atomization and address the challenges related to their integration into existing engine technologies.
The study investigates the transient behaviours of microdroplets in alternative multi-fuel blends for ICEs with a focus on biodiesel and bio-alcohol blends to explore these behaviours impact on fuel-air mixing. Experiments were conducted through introducing single fuel, binary, and ternary fuel blend droplets in various environmental temperature using LDBOS. These experiments were performed at three high temperatures: LLPT, MLPT and HLPT. The experimental fuels included diesel, biodiesel, HVO, bio-alcohols (methanol, ethanol, and octanol), and their blends. The transient behaviours of these fuel droplets were recorded using DBIMP technique, capturing key phenomena such as evaporation, nucleation, puffing, micro-explosion, secondary breakup, and combustion. Key findings of the study include
The evaporation rate of diesel-biodiesel blends was slightly lower than that of diesel-HVO blends, with pure diesel demonstrating the fastest evaporation rate.
The addition of biodiesel and HVO significantly reduced soot formation during combustion.
Blends with a high diesel content are prone to ignite at high temperatures.
In diesel-water emulsions, droplets exhibited more reactive behaviour, including rapid expansion and deformation at high temperatures.
Blends of diesel/biodiesel/HVO with alcohols (methanol, ethanol, and octanol) showed enhanced puffing and micro-explosion phenomena intend to improve fuel-air mixing.
The inclusion of water with more than 35% volume fraction, further intensified puffing and micro-explosion effects, particularly at higher temperatures.
Aerated diesel showed shorter evaporation times with longer aeration durations, highlighting the influence of aeration on evaporation behaviour.
Overall, the study suggests that heating temperature has the most significant impact on fuel evaporation, puffing, micro-explosion, and combustion phenomena, followed by fuel composition and blending ratio. These findings provide important guidance for optimizing fuel formulations to enhance fuel atomization and fuel-air mixing, presenting a promising strategy for improving the performance of alternative multi-fuel blends in ICEs
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