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Are We There Yet: Self-Management of long-term physical conditions during emerging adulthood
Full text linkSelf-management is a dynamic process of self-regulation by a person to manage the day-today consequences of living with long-term physical conditions (LTPC). Emerging adulthood, the transition between adolescence and adulthood, is a time of optimism with many life
transitions, that brings different challenges and responsibilities. Arnett’s Theory of Emerging Adulthood identifies five distinctive features of this developmental stage: identity exploration, instability, self-focus, feeling in-between, and possibilities. Across three studies, this research aimed to examine do emerging adults with and without LTPC differ in the
distinctive features of emerging adulthood, and how do these features influence selfmanagement behaviours among those living with LTPC. The first study, a systematic review of 30 papers found that emerging adults living with LTPC experience the five features of emerging adulthood within the context of their self-management, but that emerging adulthood
can pose distinctive challenges to successful self-management, highlighting a need for comparative, quantitative research between emerging adults living with and without LTPC. Consequently, the second study, a secondary analysis of the Growing Up in Ireland dataset
(n=4788), examined and found differences in those living with LTPC and their peers on factors linked with the five features of emerging adulthood. The third study, a cross-sectional survey (n=642) specifically examined Arnett’s features of emerging adulthood in emerging adults living with and without LTPC and the relationship between emerging adulthood and
self-management for those living with LTPC. Whilst emerging adults with LTPC experience emerging adulthood similarly to their peers, those with higher levels of instability reported a lower ability to self-manage their condition. This thesis provides valuable evidence that for
emerging adults living with LTPC, the developmental stage of emerging adulthood can act as a risk factor for the self-management of their LTPC. These findings can inform future research, healthcare practice and government policy
A Novel Framework for a Sustainable and Equitable Energy Transition Assessing the True Value of Renewable Energy Projects
No full textThe study is motivated by the global urgency to accelerate the deployment of renewable energy technologies, for example, green hydrogen, to achieve the 2030 and 2050 decarbonisation targets
set by the Paris Agreement and the European Union. Despite the known decarbonisation potential of renewable energy, the development of these projects has been slow, with hydrogen projects facing significant barriers related to high costs, market uncertainty, and a lack of policy support.
Through a combination of literature review, case studies, and empirical analysis, the research identifies these barriers and reveals the limitations of current project assessment tools that focus primarily on financial returns or carbon footprint reduction. This research presents the
development of a novel framework, the True Value Framework (TVF), designed to assess renewable energy projects beyond traditional financial metrics by incorporating ecological and social impacts. This was necessitated by the systemic undervaluation of renewable energy projects under conventional metrics which fail to integrate planetary boundaries and social equity needs. Existing tools, while useful for narrow cost-benefit analyses, inadequately address the interconnected nature of the climate crisis – an identified gap the TVF bridges by grounding its design in Doughnut Economics, Circular Economy, and Degrowth principles. The TVF shifts the focus toward the broader value that renewable energy projects can offer, particularly in relation to planetary boundaries and contributions to local communities. It attempts
to reconcile the tension between immediate financial gains and long-term socio-ecological viability by offering a counter narrative to growth centric energy transitions which ensures that projects are evaluated as part of a broader systemic shift rather than isolated market issues that
requires market-based fixes. The framework is tested and validaded through case studies of wind hydrogen projects and community benefit funds. The findings demonstrate that prioritising
decarbonisation and social benefits can lead to long-term economic returns, supporting a transition to a sustainable energy system. The TVF provides decision-makers with a high-level view of the
true value of renewable energy projects, facilitating more informed investment and policy decisions that align with both environmental and social goals. This research contributes to the body
of knowledge by offering a new methodology for assessing the value of renewable energy projects, ultimately aiming to support the large-scale adoption of these technologies in the context of global
climate action
Domain Adaptation of Neural Networks for Medical Imaging under Limited Data Constraints
Full text linkMedical imaging analysis has advanced significantly due to developments in computer vision. However, deep learning models are typically trained on consistent data distributions, which hampers generalizability when evaluated on datasets with varying distributions. This issue is especially prominent in medical imaging, where heterogeneity arises from differences in acquisition sites, imaging protocols, scanner types, and patient demographics. Additionally, strong performance of neural networks is linked to the availability of large, labeled datasets. However, annotated data is scarce in medical imaging, and domain expertise is not readily available, further hindering robust model development.
This research addresses these challenges by proposing novel domain adaptation methods to improve neural network generalization across diverse medical imaging domains. The methods achieve effective adaptation while minimizing the dependency on large labeled datasets, addressing the limited data availability in real-world medical settings.
This work has developed three alternatives to supervised domain adaptation, with several key innovations: (1) A novel, unsupervised, parameter-efficient domain adaptation framework for multi-target medical imaging domains is proposed. It overcomes the limitations of supervised training and the scarcity of labeled data. (2) A novel test-time adaptation framework to adapt natural foundation models, enabling zero-shot transferability to medical tasks without relying on labeled data. It
addresses several key challenges: the need for supervised training, domain-specific fine-tuning, the unavailability of annotated data, lack of domain expertise, and computational constraints. (3) A few-shot learning framework is proposed to adapt foundation models for fine-grained medical tasks, highlighting the intrinsic limitations of foundation models when applied to complex medical tasks.
These frameworks have improved our understanding of how domain adaptation can be effectively utilized for medical imaging analysis with limited labeled data and high data variability. This thesis serves as a valuable resource for medical practitioners and tool developers in designing innovative algorithms and applications for healthcare
Learning-based Methods for Optimising Shared Mobility Systems with Multimodal Data
Full text linkThis thesis explores the use of learning-based methods in Shared Mobility Systems (SMS), utilising multimodal data to address three key operational challenges: improper parking behaviour, energy consumption prediction, and pollution-aware routing. The overarching goal is to improve the efficiency, sustainability, and user experience of SMS through data-driven, task-specific solutions.
The first challenge is addressed by developing U-Park, a user-centric parking recommendation system. It predicts trip destinations and parking availability in real time using multimodal inputs, including partial trip data, GPS trajectories, and environmental features. Combining an attention-based RNN and a contextualised parking model, U-Park improves the chances of finding available parking by up to 29.66%.
The second contribution focuses on privacy-aware energy consumption modelling for shared battery electric vehicles. A Federated Learning (FL) framework enables model training across distributed data sources without sharing raw data. FL algorithms and local models are evaluated on multimodal features such as speed, altitude, and derived variables. The proposed FedAvg-LSTM model reduces mean absolute error by up to 67.84% and supports deployment in edge-cloud environments.
For the third challenge, a pollution-aware route planning system is introduced. Multimodal data from fixed and mobile air quality sensors is used to construct a high-resolution PM2.5 map, combining temporal imputation with spatial interpolation. Models including IDW, RF, LSTM, and Conv-LSTM are evaluated for short-term forecasting. The resulting pollutant maps inform route selection, reducing average exposure by 25.88% with minimal extra travel distance.
These contributions highlight the value of integrating multimodal data and adopting tailored learning approaches. The thesis also discusses challenges such as data sparsity, integration uncertainty, and model explainability, and outlines future directions including ensemble learning, uncertainty-aware modelling, and multi-objective optimisation
“Just Go Softer.” Illuminating the lived experiences of post-primary teachers embedding counselling skills within their role: An Interpretative Phenomenological Analysis
Full text linkResearch of embedded counselling among Irish teachers is distinctly underdeveloped. Embedding counselling skills within pre-existing helping roles widens access to emotional and mental health support in brief empathic moments of connection between helpers and help-seekers. Within education, embedded counselling can enhance teacher-student relationships with positive social and academic outcomes. This study reveals new understandings of
embedded counselling teachers’ lived experiences and addresses a gap in current research.
The study explores lived experiences of embedding counselling skills among Irish post-primary teachers. In-depth interviews were conducted with eight post-primary teachers who use counselling skills informally within their general teaching role. An interpretative phenomenological analysis (IPA) of interview transcripts was performed that ensured a deep, iterative, reflexive inquiry into
teachers’ subjective and individual experiences. Through cross-case analysis, four group experiential themes were identified: 1.) Privileging Relationship: "All in the relating”, 2.) Working with an Additional Lens: "Reaching into my toolkit", 3.) Being a Helpful Presence: "Being properly there" and 4.) Navigating Identity:
"A ditch each side of the road".
Teachers’ accounts illustrated how they worked with heightened self- and other-awareness while embodying person-centredness and prioritising teacher-student relationships to support student welfare and learning. They valued embedded counselling as an enriching addition to their role that enhanced wellbeing self-efficacy but some experienced role conflict, structural obstacles, and demanding cognitive and emotional load. Personal development, support, supervision, and ethical sensitivity emerge as important resources for teachers embedding counselling.
As the first of its kind in Ireland, this pioneering research importantly highlights opportunities and challenges of embedding counselling within teaching and contributes to existing conceptual and practice understandings. Key recommendations include counselling skills education for pre- and in-service teachers, mandated supervision, inclusion of embedded counselling within professional counselling education, and development of embedded counselling policy
Additive manufacturing of Ni-rich nitinol for superelastic properties for stent application
Full text linkNitinol, an alloy of nickel and titanium, has emerged as a promising material for biomedical applications, due to its exceptional superelasticity and shape memory effect. These unique properties allow stents to expand and contract in response to physiological conditions, providing superior performance compared to conventional metallic stents. However, traditional manufacturing techniques, such as machining and laser cutting, present significant challenges in processing nitinol due to its high work hardening rate, poor machinability, and complex phase transformation behavior. These limitations have driven the adoption of Laser Powder Bed Fusion (L-PBF) that enables the fabrication of intricate and patient-specific stent designs with greater precision and structural complexity.
Nitinol parts produced using this method often suffer from inherent defects such as high residual stresses, microstructural inhomogeneities, porosity, and poor surface finish due to the rapid solidification and localized heat accumulation during the printing process. These factors can significantly degrade mechanical performance, reducing the fatigue resistance, superelastic behavior, and functional longevity of the stent. Therefore, post-processing treatments are essential to achieve optimal functional properties. Heat treatments, including solution annealing and ageing, play a critical role in reducing residual stresses, refining the microstructure, and tuning the phase transformation temperatures which enable setting the superelastic response required for stent applications. Similarly, surface modification techniques such as electropolishing are necessary to reduce surface roughness and improve corrosion resistance, minimizing the risk of nickel ion leaching in the human body and ultimately enhancing the biocompatibility of the stent.
Optimizing process parameters in L-PBF, with effective post-processing strategies, is important to tailor the mechanical performance, surface characteristics, and biocompatibility of Ni-rich nitinol for medical applications. This study investigates the effects of L-PBF process parameters, ageing heat treatment, and electropolishing on the functional properties of Ni-rich nitinol. The goal is to enable nitinol metal additive manufacturing for next-generation stent production
Language in the age of AI technology: From human to non-human authenticity, from public governance to privatised assemblages
Full text linkLarge language models based on machine-learning technologies are reshaping linguistic contexts and understandings of language. We explore these reconfigurations by investigating discursive positionings of traditional institutional guardians of power in language in response
to these changes. Focusing on the discourse of the Real Academia Española (RAE), we show how RAE’s social functions, ways of asserting authority, and the nature, function, and rightful ownership of RAE’s standard language have been reimagined. Crucially, RAE presents
itself as a professional soft power that protects the rights of Spanish speakers. Drawing on tropes of authenticity and endangerment, it conceptualises language generated by machinelearning technologies as inauthentic and as destroying the authentic Spanish of human
Spanish speakers. We argue that these discourses are indexical of a power struggle where the role of traditional language norming institutions is reshaped in the face of sociotechnical innovations that are in the hands of global commercial companies. (Standard language, AI
technology, language academies, authority in language, big tech, Real Academia Española
Pharmacological evaluation of non-nucleotide purine derivatives as P2X7 antagonists for the treatment of neuroinflammation in traumatic brain injury
Full text linkTraumatic brain injury (TBI) is considered to be a leading cause of mortality and disability worldwide. After TBI, innate immunity is rapidly activated in response to damage-associated molecular patterns, such as ATP release,
recognised by P2X7 receptors. The P2X7-NLRP3 inflammasome axis has been identified as one of the main players in neuroinflammation. This study aimed to validate P2X7 receptors as therapeutic target for traumatic brain injury
‘Trundled Along, Almost Unheard of, Almost Inaccessible’: Understanding Access to State Compensation for Victims of Violent Crime in Ireland and the European Union
Full text linkThis thesis employs a socio-legal approach to examine the provision of state compensation for victims of violent crime in Ireland, which is provided through the Criminal Injuries Compensation Tribunal (CICT). In bringing together legal and empirical considerations on the issue of state
compensation, including perspectives gathered in 21 interviews with victims of violent crime, legal and victim support practitioners and former CICT decision-makers, I find that victims of violent crime face significant barriers and experience considerable re-traumatisation in attempting to access state compensation in Ireland. I also find, however, that advances in European Union (EU) law on victims’ rights, especially the newly developed EU right to state compensation has led to considerable improvements in decision-making at the CICT. In this manner, state compensation is shifting from a social solidarity approach to a rights-based conception of victimisation. Despite this important shift, I argue that several of the underlying challenges in relation to state compensation are likely to remain. These problems include restrictions on the type and amount of compensation available and the presence of eligibility and
procedural criteria which fail to take account of the social construction of victimhood, the consequences of criminal victimisation and the need to protect victims from secondary victimisation. Whilst this thesis finds that state compensation provides many practical and symbolic benefits to victims, additional political will and public resources are ultimately needed
to bring about the fundamental change necessary to tackle these underlying challenges and provide effective state compensation to all victims of violent crime
The impact of including autistic students in the ‘Leadership for Inclusion’ team at an Irish mainstream post-primary School: An exploratory case study.
Full text linkThis study explores the impact on its processes and outcomes of including students within a ‘leadership for inclusion’ (LfI) team, which, at the time of the study, was involved in reviewing critically and developing a range of policies and practices that affect young peoples’ experience of belonging and inclusion at a mainstream post primary (PP) school in Ireland. It explores how their participation can impact on group processes and relationships between and among team members and on outcomes, including alterations to policies and practices related to experiences of belonging and inclusion at the school concerned.
The study is important and timely in light of the relative paucity of literature and empirical investigation of how adults facilitate student voice and leadership. It draws upon existing research and literature on the value of harnessing student perspectives, particularly those of autistic students, in developing inclusive policy and practice. This evidence is used to set the context for the investigative element of the work, to investigate the impact of involving autistic students in an inclusive leadership team operating in a mainstream PP school.
The study employed a sequential case study design that used qualitative data gathering over two phases. It was guided by a conceptual framework. Phase 1 involved five Student Initiative Meetings (SIM) and five Collaborative Leadership Initiative (CLI) team meetings, with autistic students setting the agenda. Phase 2 comprised three focus group discussions with all participants of the CLI to delve deeper into Phase 1 findings and address the research questions. The findings are examined and interpreted within the context of the literature review provided.
The study highlighted key processes and outcomes that contribute to positive changes in school culture and practices. Central to these processes were the co-construction of leadership, shared decision-making, and collective responsibility among students, teachers, and leaders. The principal played a pivotal role in promoting inclusive practices, fostering genuine dialogue, and addressing systemic inequities. Teachers experienced empowerment through the development and consistent adoption of effective practices, while students gained a sense of ownership and agency, actively contributing to school decision-making and improvement efforts. Furthermore, it was identified that the creation of safe spaces and authentic student-teacher partnerships was essential for fostering relational care and safety. As a result, the outcomes included enhanced student engagement, motivation, and a greater sense of belonging, which ultimately led to a more connected and inclusive school environment. These changes collectively contributed to the transformation of school culture and the improvement of institutional practices.
This study contributes to current knowledge in the areas of collaborative leadership for inclusion at the school level, the mobilisation of learner voices, particularly those of autistic students, and the empowerment of both learners and teachers in driving sustainable change within educational settings. By challenging traditional perspectives and practices, the research highlights the potential to reduce or eliminate ‘soft barrier’, policies and practices that often hinder inclusive education in PP schools and contribute to the marginalisation of autistic and other vulnerable students. The findings provide valuable insights that can inform efforts to create more inclusive and equitable school environments. While the outcomes must be interpreted within the specific context of the study, they offer relevant applications for those interested in fostering inclusive practices in other educational settings. The study thus advances understanding in areas that promote collaborative leadership, elevate the voices of marginalised learners, and empower teachers and students to enact lasting, positive change in school