Edinburgh Napier University

ICD-11 presents updated descriptions for single episode and recurrent depressive disorder. A depressive episode (DE) is integral to these disorders and contrasting descriptions of the structure of DE symptoms are presented in ICD-11 and the Clinical Descriptions and Diagnostic Requirements for ICD-11 (CDDR). The former presents symptoms as unidimensional while the latter suggests symptoms are multidimensional reflecting affective, cognitive-behavioural, and vegetative clusters. This study examined if the latent structure of the DE symptoms is better represented by a one- or three-factor model. Data were collected from general population samples from the United Kingdom (UK: N=975) and Ukraine (N=2050). DE symptoms were measured using the International Depression Questionnaire, and the latent structure of the DE symptoms was assessed using confirmatory factor analysis. The three-factor model of DE symptoms outlined in the CDDR performed substantially better than the one-factor model and was a close fit to the UK and Ukrainian data. This model was invariant for sex in each nation. Prevalence estimates based on a three-factor model were similar to those based on the ICD-11 requirements, and similarly sized sex differences were observed. The multidimensional description of the DE symptoms outlined in the CDDR better represented data from two culturally distinct samples. Discrepancies in how DE symptoms are described in ICD-11 and the CDDR should be resolved to facilitate future research efforts.

With Complex Post Traumatic Stress Disorder (CPTSD) as a new diagnostic category, there is a diversity of interventions and influences on clinical practice, it is prudent that we gather information about current 'treatment as usual'. This study aims to address this need by describing the current clinical landscape of CPTSD treatments offered in NHS services in Scotland. An online survey was distributed via heads of service managers to clinicians working with adults. The survey posed questions about psychological therapy models, professional experience and, for the clinicians' latest 3 discharged cases, the length of therapy and perceived patient outcomes. Forty-nine clinicians, most of whom were clinical psychologists, took part and provided data about 139 clinical cases. Twenty-three different therapeutic models were cited by clinicians, with phase-based approaches described by 64% of clinicians. The modal number of sessions was 21-30. Most cases were described as completed, with 28% ending before therapy was completed, and a further 7% dropped out. Of those who completed therapy, 86% were judged to have improved after therapy, with 11% judged to have deteriorated. There is a wide variety of treatments available for complex PTSD, and innovative interventions and trials are required to support future clinical decision-making about the optimal treatment components and therapy length.

Federated learning (FL) enables collaborative medical image analysis across decentralized institutions while preserving data privacy. However, real-world deployment faces critical challenges: modality heterogeneity, where clients possess incomplete or varying medical image modalities, and cross-disease generalization, requiring models to adapt to unseen pathologies with anatomical consistency. Existing methods like FedAvg, FedProx, IOP-FL, and PntTranForFL assume uniform modality availability and static client participation, leading to poor performance under realistic clinical constraints. We propose TADynFed, a novel framework for the Heterogeneous Federated Learning (HFL) paradigm, which addresses both data and modality heterogeneity through, a tissue-aware disentanglement strategy that decouples modality-tailored and modality-shared features, a dynamic prototype memory bank for missing modality compensation, an adaptive aggregation mechanism that accounts for client reliability and tenure. We evaluate TADynFed using multi-disease MRI datasets including BraTS21 along with cross-domain imaging data from CheXpert (chest X-ray) and Hep-2 (microscopy), simulating a 13-client FL environment. The proposed framework achieves an average mDice score of 66.03%, significantly outperforming baseline methods such as PointTransformerFL at 58.60% and FedAvg at 53.06%. It also attains the lowest boundary alignment error with an ASD of 1.85 mm and HD95 of 8.70 mm, surpassing existing approaches by notable margins. In terms of calibration stability, TADynFed records an ECE of 0.09, indicating superior confidence reliability. Furthermore, it demonstrates high communication efficiency with only 76 MB of data exchanged per round, compared to 95-105 MB in other frameworks. These results validate TADynFed's ability to maintain high segmentation accuracy, boundary precision, and calibration stability while minimizing bandwidth usage. Outperforming existing frameworks by significant margins, TADynFed demonstrates robust boundary alignment, superior calibration, and efficient communication. It also exhibits strong cross-disease transferability without retraining. By integrating structured representation decomposition, prototype-guided fusion, and client-adaptive learning, TADynFed establishes a new benchmark in realistic, heterogeneous, and mix-modal federated medical imaging systems. Code supporting this study TADynFed.

Developing a circular economy (CE) for construction and demolition waste (CDW) presents a promising pathway to decarbonize the construction industry by reducing reliance on carbon-intensive primary material production. However, most existing studies rely on life cycle assessment (LCA) approaches and treat CDW flows as isolated processes, overlooking the broader economy-wide emission consequences and distributional effects arising from complex inter-sectoral and inter-regional material and energy flows. This limitation risks underestimating the full carbon mitigation potential of CDW circularity, thereby hindering progress toward the Net-Zero targets pledged by most global economies. This study introduces a novel analytical framework integrating LCA with environmentally extended input-output (EEIO) analysis to quantify the environmental impacts of CDW circularity, explicitly accounting for sectoral and regional trading linkages. Applying the framework to the United Kingdom (UK), this study estimates carbon emissions and potential savings under multiple CDW circularity scenarios for the year 2018. The results indicate that material use for domestic final demand and exports generates approximately 159 and 170 million tonnes of CO₂, respectively, while CDW circularity achieves only modest emission savings of 2–3 million tonnes (<1%). Reuse scenarios deliver greater reductions than recycling, with the most significant benefits observed in secondary and tertiary industries, particularly from the circular use of metallic and wood materials. Despite the UK's high CDW recovery rate, inefficient treatment pathways and weak alignment between recovered-material supply and industrial demand constrain the net-zero potential of CDW circularity. Enhancing recovery efficiency, advancing cleaner technologies, and improving material productivity are therefore critical for supporting the UK's net-zero transition. This study is novel in pioneering one of the first LCA–EEIO analytical frameworks for evaluating the economy-wide environmental impacts of CDW circularity, providing a scalable methodological foundation and system-level evidence for global economies seeking to accelerate their pathways toward Net-Zero targets. • Developed a framework to assess carbon impacts of CDW circularity and trading. • Integrated environmentally extended input–output (EEIO) analysis and LCA. • The UK was used as a case to offer policy implications and validate the framework. • Carbon savings from CDW circularity and trading are rather limited in UK (<1%). • Greater carbon savings from reuse than recycling, and from metals than others.

Acts of commemoration construct narratives of collective memory and identity, shaped by organisers’ agendas. Existing literature presumes that organisers primarily use commemoration for national political, social and cultural outcomes. Contemporary commemoration, however, takes place in times of a contested role of the nation for collective memory and identity, while events are commonly used for economic outcomes in addition to political, social and cultural ones. There is hence not enough research that explores the roles and uses of contemporary commemorative events. Drawing primarily on literature from the nascent fields of memory studies and event studies, this qualitative constructionist research explores how narratives of collective memory and identity emerge at commemorative events of the fall of the Berlin Wall in the major anniversary years of 2009 and 2014. These events are an interesting and suitable context for the research as they were the first events of this kind and commemoration of the Wall poses various challenges due to the Wall’s shifting meanings. Findings from a semiotic analysis of the events suggest that these events construct narratives beyond the national dimension. By interpreting the historical events to be rooted in Berlin and of international significance, strong local and international identity narratives are constructed. Findings from a thematic analysis of documents and interviews with organisers illustrate that organisers use the events for branding and event tourism development. This research argues that such emerging uses of commemoration play a significant role for the commemorative narrative. The findings further illustrate the permeable nature of the state-sponsored narrative in Berlin and the now consolidated role of Wall-related memory for local identity construction. The research contributes to the theoretical understanding of commemorative events in general and Berlin Wall commemoration in particular, as well as of contemporary German national identity. It further makes a methodological contribution on the use of semiotics in this context. An applied contribution on implications for the management of commemorative events is also made.

Students experience stress due to many factors including educational unpreparedness, financial strain and the inability to integrate socially. This mixed methods study aimed to investigate stress levels of undergraduate students in a post-1992, Scottish university and the potential for measures of stress to act as an indicator of student withdrawal. Additionally, the project was concerned with students’ use of support services and the development of a resource to facilitate student resilience with the aim to impact positively on retention. The level of perceived stress reported by students appeared to be high and was coupled with intention to dropout across all study levels. Students’ psychological wellbeing appeared to be much lower than results published for the general population and actual withdrawal within the sampled cohort was higher than the University’s formal figures would suggest. Perceived stress predicted a student’s intention to withdraw but this association did not transfer to actual withdrawal suggesting that other factors, most likely coping mechanisms, play a part in mediating the withdrawal behaviour. Further data collection is required to confirm if a combination of perceived stress and coping data more accurately predicts actual withdrawal, however results showed that measures of perceived stress could be used to indicate a proportion of ‘at risk’ students. Low use of avoidance and distraction coping was a better predictor of low self-reported stress than was high use of adaptive coping and this may have implications for interventions that endeavour to reduce stress through improved coping. Despite the seemingly high levels of stress and potential worry over dropout, students were reluctant to seek support and many were unaware of the support services available. An online, stress education resource was developed to build students’ understanding of stress and the support available. It was envisaged that this would reduce stigma, aid in student self-awareness and self-assessment and improve their coping repertoire. The intervention was trialled alongside controls and results demonstrate that further work needs to be done to embed stress resilience into the student life cycle. The project reaffirms the need for concern over student wellbeing and highlights areas for improvement. Given students in this study may be considered ‘engaged’, results bring to light a population, previously thought to be ‘low risk’, but who could benefit from additional support to prevent unnecessary underachievement or attrition.

This thesis sheds light on Dollie Radford as one of the talented women writers whose work is still insufficiently acknowledged by contemporary studies because of the lack of extant information about her life. LeeAnne Richardson, Ruth Livesey, and Emily Harrington are three of only a handful of scholars who have discussed in any detail Radford’s role as a poet, socialist, and activist who was surrounded by key figures in the history of English literature and culture, such as William Morris, Oscar Wilde, Eleanor Marx, and Olive Schreiner. Despite being identified by Victorian reviewers as a “domestic” woman poet, all contemporary scholars who have hitherto considered Radford pinpoint her “radical” thoughts and engagement with the New Woman. Building on arguments by Radford’s contemporary scholars, my argument highlights Radford’s role as a Victorian feminist who sought, through her poetry, to challenge patriarchal attitudes and defy social conventions which imprisoned women of her generation. While the first two chapters of this thesis provide a contextual background of women’s rights and women’s poetry in the Victorian era, the four remaining chapters explore how Radford’s personal conflict as an ignored married woman and unsupported writer might have influenced her empathetic portrayal of marginalised figures, such as prostitutes, the working classes, women writers, and homosexuals. Simultaneously, the chapters highlight the subversive meanings obscured by Radford’s use of evocative and aesthetic language. The majority of the poems, letters, and diary entries included here are unpublished and have not yet been considered by contemporary critics. Thus, this research adds to the existing body of knowledge, offering a new approach to Radford’s life and poetry in relation to aspects concerning women in Victorian and Edwardian England. By continuously interrogating Radford’s choice of metaphors and images in contrast with those depicted by other Victorian poets, I aim to establish Radford as a significant fin-se-siècle woman poet whose poetry embraces a literary tradition which questions negative gendered attitudes biased against passionate women writers.

Advancements in agricultural technologies have increasingly emphasized technical innovations aimed at improving the predictability and reliability of agricultural outputs. These aspects encompass developments in agricultural machinery, automation technologies, biotechnology, and controlled environment farming systems. This article focuses on Remote Sensing (RS)-based approaches applied to agricultural yield estimation for both crops and plants. RS technologies offer enhanced precision and scalability, making them particularly effective for large-scale agricultural monitoring and analysis. A systematic classification of RS-based methodologies employed for crop yield estimation is presented in this study. These methodologies are categorized into: (i) Sensor-Based approaches, (ii) Platform-Based approaches, (iii) Analytical and Modeling-based methods, and (iv) Machine Learning (ML)-driven models. Based on findings reported across multiple studies, it is observed that Deep Learning (DL)-based architectures consistently achieve superior performance across key evaluation metrics, including accuracy, precision, recall, and F1-score. This performance advantage stems from their capacity to learn hierarchical representations, capture complex non-linear relationships, scale efficiently with large datasets, and reduce reliance on manual feature engineering. Following this classification, our article presents a comprehensive discussion of the limitations associated with these methodologies. These challenges are organized into four major categories: (i) Environmental, (ii) Algorithmic, (iii) Hardware and Operational, and (iv) Wireless Sensor Networks (WSNs) related limitations. The adopted classification framework helps readers identify and address the key challenges associated with effective yield estimation in crops and plants. Moreover, the article concludes by outlining several future research directions intended to support and guide both early-career and experienced researchers in this domain.

The United Kingdom published a legally binding document to reduce national greenhouse gas emissions by year 2020 up to 34% against the 1990 levels. This target also fulfils the Europe 2020 strategy of 20% carbon emission reductions by year 2020 (EC, 2010). Emissions due to space heating count for around 60% of the total domestic emissions (DCLG, 2012). The report “Rethinking Construction” published in 1998 emphasised the opportunities to improve the quality and efficiency of the UK construction sector (Egan, 1998). More recently, a framework has been published by the Government to tackle fuel poverty by building more energy efficient homes (DECC, 2015). In terms of energy performance, Passivhaus is recognised as one of the most energy efficient and researched construction standards which requires an exceptionally high-level of insulation and airtightness. Closed-panel timber frames are a relatively new system in UK with an opportunity for growth. These advanced panels are pre-fitted in the factory, reducing the on-site work. However, closed-panel systems present a more complex sole plate fixing detail which can have an undesirable long-term impact on the structural and thermal performance of the building. The work presented in this thesis investigates the structural considerations, racking performance, of timber frame closed panel systems for future building regulations. The thesis underlines the significance of structural stability, serviceability and detailing in relationship with long-term thermal efficiency and airtightness, according to Passivhaus standard. An experimental study was carried out to investigate the structural racking performance of advanced closed panel systems. A comparison was made between the behaviour of the timber frame panels and the analytical PD 6693-1. A set of different wall panel built-ups is presented for optimised Passivhaus design, including thermal bridge-free sole plate details. A timber frame racking software application was developed to optimise the structural design of shear walls. A parametric study was carried out with this tool to generate efficient timber frame wall design tables for different applied racking loads and U-values. The software application also allows for direct specification of robust sole plate base fixings and thermal bridge free details.