New materials with optoelectronic and energy conversion properties

Abstract not currently available

Developing a Theory- and Evidence-Based Self-Management Intervention for Multimorbidity in Thailand (M-SMiTh): an intervention mapping approach

Abstract not currently available

1,1-dithiolate ligands in coordination chemistry: a study of their electronic and molecular structures

Dithio-ligands are known for their versatility in coordinating to various metal centres. Among them, 1,1- and 1,2-dithiolates stand out for their extended conjugated backbone and unusual redox properties upon coordination. While early studies have shown clear differences in bonding and redox behaviour between the two systems, 1,1-dithiolates remain far less explored. A series of new NiII and CuII bis-1,1-dithiolate complexes were prepared and compared alongside other homoleptic dithio-complexes. The 1,1-dithiolate ligand scope was expanded to the unprecedented para-R- phenylacetonitrile dithiolate R-padt2− derivatives (R = OMe, H, Cl, meta-CF3, para-CF3, CN, NO2), enabling a systematic variation of the electron-donating and electron-withdrawing substituents on the backbone. All complexes were characterised by ligand-specific bands in their IR spectra and chemical shifts in their 1H/13C NMR spectra. In their EPR spectra, the characteristic profile of a CuS4 centre was observed and their electronic absorption spectroscopy was characterised by ligand-to-ligand charge transfer (LLCT) as well as ligand-field (LF) transitions. Additionally, a reversible oxidation process was observed during cyclic voltammetry experiments and was proved to be formally metal-centred. The square-planar structure was confirmed by X-ray crystallography, and DFT calculations provided further insight into their MO energy levels and bonding. By using the Hammett and pKa values, predictable substituent effects were confirmed across the series, creating a correlation between the molecular and electronic structure of these complexes. Oxidation of the CuII 1,1-dithiolate complexes stabilised the formal CuIII state and the potentials were directly affected by the substituent effects of the backbone groups of each 1,1-dithiolate. The final part of the thesis extended to studying the chemistry of 1,1-dithiolate ligands in heteroleptic NiII and CuII complexes containing α-diimines and the non-conjugated N-donor ligand tetramethyl ethylenediamine (TMEDA). The NiII 1,1-dithiolate–diimine complexes exhibited distinct LLCT transitions containing the HOMO and LUMO, as well as diimine-based reductions. The energy of the LLCT and the potentials of the reductions showed correlations with the Hammett and pKa values corresponding to the α-diimine and 1,1-dithiolate coordinated ligands. Electron-withdrawing substituents on the α-diimine ligands stabilised the π* orbitals, leading to red-shifted LLCT transitions and more positive reduction potentials, whereas electron-donating groups had the opposite effect

Cardiovascular disease in Type 2 Diabetes Mellitus: A precision medicine approach applying artificial intelligence for heart failure and mortality prediction

Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide, despite substantial advances in diagnosis and treatment. People who suffer from cardiovascular disease often have multiple risk factors and other chronic conditions. Additionally, medical events may be strongly influenced by socioeconomic status. Patient information can be obtained from electronic medical records (EMRs) that, unlike data from clinical trials and registries, provide a broad range of patient characteristics representative of the general population. EMRs covering a population of ~1.1 million people in Greater Glasgow & Clyde (GG&C) Health Board NHS over 50 years (the age at which the incidence and prevalence of disease affecting older people increase rapidly) were used. Information such as demographics, laboratory tests, primary-care prescriptions, hospitalisations and mortality was retrieved. Several steps were required to ensure that the extracted information was appropriate for analysis and transformed for investigations beyond traditional statistics. Accordingly, data on patients with type-2 diabetes mellitus (T2DM) were obtained to examine their health trajectories, including, incident heart failure and death. Novel risk prediction models were built to help understand the development of heart failure (HF) in patients with T2DM. The models were developed using random survival forest (RSF) methodology. This research highlights the limitations of traditional regression models and demonstrates the improvement of risk prediction with RSF methods, which outperformed traditional approaches in both discrimination and calibration. State-of-the-art machine learning interpretation was applied to discover key contributing factors to the development of heart failure and to all-cause mortality. External validation was applied by acquiring EMRs from Hong Kong, Special Administrative Region (SAR) China. The inclusion of two diverse populations found little evidence of ethnicity-related differences in risk factors. GG&C key risk factors for incident HF were loop diuretics, atrial fibrillation (AF), history of coronary artery disease (CAD), older age, lower levels of estimated glomerular filtration rate (eGFR), haemoglobin and serum albumin. Similarly, for Hong Kong, key risk factors were use of loop diuretics, insulin, lower serum albumin, haemoglobin, lymphocyte counts and eGFR. The model based on Hong Kong data showed slightly better performance compared to the Glasgow cohort for incident heart failure (C-index 0.88 and 0.87) and all-cause mortality (0.85 and 0.83). In both cohorts’ older women were more likely to be prescribed loop diuretics. Whether loop diuretics are just a marker of undiagnosed heart failure or whether they accelerate the progression of cardiovascular and renal disease is uncertain. Another key similarity was that patients had prevalent chronic kidney disease (CKD) events in the prescribed loop diuretics groups. Treatment with loop diuretics was strongly associated with all-cause mortality in GG&C and Hong Kong. (GG&C: adjusted hazard ratio: 2.93, (95% CI: 2.821 to 3.04); Hong Kong: adjusted hazard ratio: 1.75 (95% CI: 1.72 to 1.77). Only a minority of patients prescribed loop diuretics had a diagnosis of heart failure, end-stage renal disease or resistant hypertension. Finally, further investigation of social deprivation in GG&C underlined that 41% patients with T2DM were in the most deprived socioeconomic quintile and that they had a 36% higher rate for all-cause mortality compared to those who were least deprived (adjusted HR: 1.36, 95% CI 1.24–1.50, p < 0.005)

Exploring global citizenship development: from global, to local, to the self

Global citizenship (GC) has long been promoted as an educational panacea for a plethora of global crises – from environmental degradation to poverty and war. While garnering popularity, GC has simultaneously evolved into a conceptually ambiguous and contentious concept from the perspectives of both advocates and critics. Further, global citizenship education’s (GCE) historical overemphasis on international mobility pedagogies is considered problematic because such programmes are cost prohibitive and findings from studies attempting to measure the efficacy of such programmes have been mixed. The current study sought to redress the GCE gap between educational aspirations and observed manifestations by investigating GC from the perspectives of diverse GC actors. Under an interpretivist lens, and via an exploratory sequential mixed-methods approach, this study uncovered critical methodological blind spots in prior GC research. The triangulation of quantitative and qualitative data from a scoping audit, survey questionnaire and life-history interviews, enabled the untangling of dominant (idealised and abstracted) conceptions of how GC ought to be from how it is actually embodied (in practice) through observable attitudes, values and behaviours (AVBs). The ‘Prevalence of Ambivalence’ theme that emerged from a reflexive thematic analysis (rTA) confirmed that it is problematic to assume that individuals who work in the field of GC identify as global citizens, embody GC or are even knowledgeable about it. By making such presumptions, I argue, previous studies have stripped research participants of their personal agency and exacerbated the GCE gap by conflating injunctive and descriptive norms. Contrast analysis of self-identifying and nonidentifying global citizen perspectives revealed that key GC actors are not necessarily practising what they are preaching in that not one interviewee appeared to (or claimed to) embody every dimension of GC currently promoted by international organisations (e.g., UNESCO, PISA and Oxfam). The unbounded and longitudinal aspects of the life-history interviews additionally revealed that critical transformative experiences were mainly associated with what would appear to be seemingly mundane everyday interactions or occurrences. By illuminating successful, locally contexualised pathways to global engagement and global citizenship identification (GCID), which were not predicated upon international mobility experiences, this study has identified more readily accessible roadmaps to GC for educators than proffered by prior literature. Perhaps the most notable discovery this study highlights, however, is potential backlash effects of an ‘Enlightened’ GCID which confound the previously purported relationship between GC identification and embodiment. From this study, it appears that GCID is neither necessary nor sufficient to engender GC AVBs. Based on findings from this study, I argue that preoccupations with fostering global superordinate identities may be counterproductive. This study has provided evidence that GC should be conceptualised in terms of three empirically distinct domains of enactment: identification, embodiment and promotion. The aforementioned findings have significant implications for GC policymakers, researchers and practitioners as well as aspiring global citizens. There appears to exist the propensity for a global citizenship that emanates from self-interest rather than altruism and provides scope for essentially any individual to help make the world a better place in their own capacities and their own contexts

Advancing drought understanding and prediction in the Vietnamese Mekong Delta

Drought, one of the most destructive climate related natural hazards, affects millions of people worldwide and poses substantial challenges in the Vietnamese Mekong Delta (VMD), one of Southeast Asia’s largest deltas. In recent decades, particularly during 1991 1994, 1998, 2005, 2010, 2015 2016, and 2019 2020, the VMD has suffered from severe and prolonged droughts that resulted in significant socioeconomic impacts. In this delta, droughts often result in severe clean water shortages and extensive damage to cropland. Despite these profound impacts, the mechanisms driving these droughts, including anomalies in the atmospheric moisture transport and land atmosphere (LA) interactions, and the prediction of droughts in the VMD remain underexplored. Addressing these gaps is crucial for enhancing drought preparedness and developing effective drought mitigation strategies. Accordingly, this thesis aims to achieve three primary objectives : 1) to elucidate the sources of precipitation moisture and identify the dominant factors influencing these sources during drought periods in the VMD ; 2) to quantitatively assess the LA interactions in the VMD using advanced deep learning techniques; 3) to develop an accurate deep learning model capable of predicting droughts in the VMD on account of atmospheric conditions from the external precipitation source region. The first two objectives are designed to deepen understanding of the mechanisms and processes driving droughts in the VMD, while the third aims to utilize these insights to provide accurate drought predictions. To better understand the process es of atmospheric moisture transport, the Water Accounting Model-2layers (WAM-2layers), an Eulerian based moisture tracking model, was employed to identify the primary moisture sources of precipitation in the VMD from 1980 to 2020. In addition, for the first time, the causal inference algorithms were introduced to analyze the causal relationships among variables involved in moisture transport, specifically, to identify which factor drives the moisture transport process and dominates the amount of tracked moisture. The analysis revealed that (1) over 60% of precipitation in the VMD originates from external moisture sources (60.4 93. 3%), with local recycling contributing from 1.2 % to 27.1%; (2) seasonal shifts in monsoon patterns strongly influence the origins of moisture: during the dry season, the South China Sea (northeast) serves as the dominant source, while the Bay of Bengal (southwest) becomes the primary origin during the wet season; (3) based on the causal inference algorithms, atmospheric humidity and wind speed in the upwind area were identified as the principal factors influencing moisture transport during dry and wet seasons, respectively; (4) large scale forcings (e.g., El Niño and La Niña) were found to affect the processes of affect the processes of moisture transport significantly and these effects vary spatially and seasonally across the VMD’s precipitationshed; (5) local atmospheric conditions, including atmospheric instability (e.g., convective available potential energy, CAPE) and local atmospheric humidity, also play a crucial role in modulating moisture recycling efficiency. As for the interactions among LA variables, the Long- and Short-term Time-series Network (LSTNet) was applied to model these dynamics over the VMD. The key findings are as as followsfollows: (1) the LSTNet model demonstrated superior performance compared to the traditional regional climate model in simulating key variables (i.e., precipitation, soil precipitation, soil moisture, sensiblemoisture, sensible and and latent heat) during both dry and wet seasons. It exhibited higher accuracy and lower bias, underscoring its suitability for modeling LA interactions in the VMD; (2) this deep learning model effectively captured the relative importance of key variables within the LA interactions, highlighting the critical roles of soil moisture and sensible heat, particularly during dry periods when their negative anomalies substantially reduce precipitation. For example, anomalies in sensible heat were found to decrease precipitation by up to 20% during dry periods, primarily through interactions with temperature and convective inhibition (CIN). Similarly, soil moisture strongly influences precipitation in both dry and wet periods, with deficits leading to reductions in precipitation of up to 30%; (3) projected declines in soil moisture coupled with increases in sensible heat are expected to exacerbate precipitation deficits under changing climatic conditions. By 2075-2099, a 10% increase in sensible heat could reduce precipitation by 3.76% in dry seasons. Finally, exploring the utility of atmospheric conditions from external precipitation source regions, the deep neural network, Convolutional Gated Recurrent Unit (ConvGRU) was developed to enhance accuracy in drought prediction. The ConvGRU model exhibited exceptional performance in predicting drought conditions at a 3-month lead time, which successfully predicts approximately 90% of meteorological drought events and about 80% of agagricultural drought events, with ricultural drought events, with fewer than 10% false predictions for drought months and events. Furthermore, ConvGRU predicts about 70% and 80% compound dry-hot months and events, respectively. The outstanding performance of the ConvGRU model in drought prediction at the 3-month lead time largely attributed to the delayed impacts of external atmospheric conditions, including specific humidity and U- and V-wind, on the VMD’s drought conditions through the water vapor transport process. Incorporating the atmospheric data from these external precipitation source regions significantly improvess the ConvGRU model’s ’s predictive capability, particularly at the lead time of 3 months. In summary, this research not only advances the understanding of mechanisms driving drought dynamics including external atmospheric moisture transport and local LA interactions, but also establishes an innovative, effective model for drought prediction. These research developments are vital for improving drought resilience and adaptability in the VMD, and offering substantial benefits for regional drought management strategies

Federated learning for next generation intelligent applications

The rapid proliferation of smart devices and Internet of Things (IoT) technologies has revolutionised data collection for artificial intelligence (AI)-driven applications, enabling rapid training and near real-time inference. However, the traditional centralised learning approaches require transferring vast amounts of raw data from end devices to a central server. This process leads to substantial network overhead, increased latency, and significant privacy concerns, hindering the scalability and responsiveness of intelligent applications. This thesis exploits federated learning (FL) as a distributed, on-device learning framework that enables collaborative model training without raw data sharing. The distributed architecture of FL offers privacy by design and reduces communication costs by exchanging the model parameters that align with principles of data sovereignty and regulatory compliance. Despite its advantages, FL faces significant challenges in real-world applications, and this thesis aims to address the following three critical challenges: C1) data diversity; C2) robust aggregation ensuring privacy and security in the training process; and finally, C3) energy efficiency. The first contribution introduces the similarity-driven truncated aggregation (SDTA) framework, designed to tackle challenges C1 and C2. SDTA measures the similarity among the model updates to identify and filter the anomalous updates, mitigating the impact of attacks and overfitting without accessing client data. Additionally, it incorporates differential privacy (DP) to strengthen training privacy. The second contribution introduces the semantic-aware federated blockage prediction (SFBP) framework, addressing challenges C1 and C3. Using multi-modal fusion and a lightweight computer vision model for edge-based semantic extraction, the proposed framework reduces communication costs and inference delays while maintaining high prediction accuracy. Additionally, SFBP incorporates a filter mechanism to minimise the effects of noisy or adversarial updates. The third contribution addresses C1 and C3 and develops a hybrid neuromorphic federated learning (HNFL) framework for outdoor human activity recognition (HAR) using wearable sensors. The proposed spiking-long short-term memory (S-LSTM) model combines the energy-efficient spiking neural networks with the sequential data handling strengths of LSTM networks. This approach improves the accuracy while ensuring data privacy and reducing computational costs, making it suitable for deployment on resource-constrained edge devices. Finally, to address challenges C2 and C3, the federated fusion quantisation (FFQ) framework is proposed to improve HAR models in indoor settings. FFQ combines FL with edge-based preprocessing, feature engineering, and model compression to achieve a low false positive rate, essential for applications like fall detection. A customised FedDist algorithm is used for global model aggregation, effectively reducing overfitting in diverse data. Additionally, FFQ applies model compression and quantisation-aware training to lower communication overhead without compromising accuracy. These contributions advance FL by enhancing scalability, robustness, and efficiency, paving the way for next-generation intelligent systems

Enhancing robustness and adaptability in motion intent recognition: a multimodal approach with advanced neural networks and meta-learning

Abstract not currently available

Machine learning for analogue media damage restoration

Analogue media degradation presents a unique challenge for digital restoration, requiring the disentanglement of physical damage from intended content and style. This thesis investigates machine learning approaches for detecting and segmenting damage across diverse media types, addressing a critical bottleneck in restoration workflows. We first focus on the constrained setting of a single type of analogue media, and develop a statistical framework for modeling film damage. Through perceptual studies, we demonstrate that our approach generates training data indistinguishable from authentic damage. Using this data to train supervised models yields significant improvements in damage detection performance, as validated on our benchmark dataset of professionally restored high-resolution film scans. Expanding beyond film, we introduce ARTeFACT, the first comprehensive dataset for analogue media damage detection comprising over 11,000 pixel-accurate annotations across 15 damage categories and 10 diverse media types. Systematic evaluation reveals that state-of the-art supervised segmentation methods, including foundation models like Segment Anything, fail to generalize across different media and struggle to disambiguate damage from visually similar content features. To address these limitations, we investigate the semantic understanding capabilities of text to-image diffusion models. We develop a novel unsupervised , zero-shot semantic segmentation framework leveraging self-attention mechanisms in Stable Diffusion, achieving state-of-the-art performance on standard benchmarks. Through matrix exponentiation of attention maps, we provide a principled control mechanism for segmentation granularity based on random walk theory. Such mechanism is crucial for enabling segmentation of artefacts at varying granularity. Our findings demonstrate that effective analogue media damage detection requires moving beyond rudimentary pattern recognition toward semantic understanding of content-damage relationships. This work establishes a methodological foundation for automated restoration systems that can support heritage preservation at scale while respecting the unique characteristics of different analogue media. The challenges presented by damage detection further illuminate fundamental questions about representation learning and visual-semantic disentanglement that are significant for advancing machine learning beyond pattern recognition toward more meaningful visual understanding

Analysing the serological response to feline coronavirus and potential for cross-reactivity with SARS-CoV-2

Abstract not currently available