Robust highly conductive patterns in flexible PEEK materials with either sp3 or sp2 dominant carbon phases produced by using ultrashort laser pulses

This study reports ultrashort direct laser writing of highly conductive patterns on 50 µm thick flexible polyether-ether-ketone (PEEK) using two distinctive, electrically conductive regimes confirmed by XPS; an sp3-carbon dominant regime with minor sp2 contribution (Phase-I) and an sp2-carbon dominant regime with a small sp3 contribution (Phase-II). Phase-I is produced using a single laser scan strategy with a narrow fluence window. Phase-II is optimally produced using four sequential laser scan passes, each with a specific fluence. The rationale for the first pass was to disrupt carbon atoms, the second and third exposures were to gently modify this disrupted phase, and the fourth pass was to anneal the final structure. No characteristic graphene peaks were observed in Raman spectra for Phase-I, however, this phase surprisingly showed higher conductivity when compared with Phase-II. Raman peaks for graphene were observed for single laser scan passes at higher laser fluences with the onset of surface damage. In Phase-II, PEEK was laser scanned multiple times to transform into sp2 graphene integrated in the form of laser induced periodic surface structures. The lowest sheet resistance obtained was 9.60 Ω/□ and 11.53 Ω/□ corresponding to an electrical conductivity of ∼4.33 × 103 S/m and 4.17 × 103 S/m for Phase-I and Phase-II, respectively. The reported low-fluence process is significant for direct laser writing of conductive structures on polymers providing a precise and controlled manipulation of carbon configuration to produce components which are not impacted by mechanical friction.The Authors wish to acknowledge the support of the European Space Agency (ESA) contract ESA AO/1–10715/21/NL/AS and Research Ireland contract 19/US-C2C/2672. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission

Synthetic face ageing: Evaluation, analysis and facilitation of age-robust facial recognition algorithms

Establishing the identity of an individual from their facial data is widely adopted across the consumer sector, driven by the use of facial authentication on handheld devices. This widespread use of facial authentication technology has raised other issues, in particular those of biases in the underlying algorithms. Initial studies focused on ethnic or gender biases, but another area is that of age-related biases. This research work focuses on the challenge of face recognition over decades-long time intervals and explores the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models in recognizing people across these longer time intervals. To achieve this, we first design a set of experiments to evaluate state-of-the-art synthetic ageing methods. In the next stage, we explore the effect of age intervals on a reference face recognition algorithm using both synthetic and real ageing data to perform rigorous validation. We then use these synthetic age data as an augmentation method to facilitate the age-invariant face recognition algorithm. Extensive experimental results demonstrate a notable improvement in the recognition rate of the model trained on synthetic ageing images, with an increase of 3.33% compared to the baseline model when tested on images with a 40-year age gap. Additionally, our models exhibit competitive performance when validated on benchmark cross-age datasets and general face recognition datasets. These findings underscore the potential of synthetic age data to enhance the performance of age-invariant face recognition systems.Irish Research Council (Grant Number: EPSPG/2020/40 and IRCLA/2023/1992

Against unreality: A literary ethics of attention to suffering with Simone Weil, Iris Murdoch, and Elsa Morante

This thesis positions literature as a privileged space for attending to suffering, examining the moral value of attention as theorised by Simone Weil and expanded by Iris Murdoch. I explore the relationship between attention and literature, establishing what I define as a ‘literary ethics of attention’. From this, I develop a framework I term ‘mystical realism’, which proposes that literature can bear ‘attentive’ witness to suffering. A case study of Elsa Morante’s novel La Storia, viewed through the lens of Murdochian philosophy of literature, serves to illustrate this idea, as the text is rooted in Weil’s concept of attention to le malheur. Through this analysis, the thesis suggests that La Storia is an example of ‘mystical’ attention to the darkest and most invisible aspects of reality, and thus a literary endeavour to restore the integrity of the real

Feeding regime selectively enriching acetoclastic methanogens to enhance energy production in anaerobic digestion systems

Applying the r/K selection theory to explore the distribution patterns of acetoclastic methanogens under varying conditions is rare, although it can offer a basis for optimizing community structure and enhancing methane production in anaerobic digestion systems. Different operating modes (sequencing batch reactors, SBRs; and continuous-flow reactors, CFRs) and solids retention times (SRTs; 15 and 50 days) were adopted to acclimate different acetoclastic methanogens in acetate-fed anaerobic reactors. SBRs exhibited a significantly higher CH4 production rate than CFRs (P = 0.037). Methanosarcina exhibited a higher relative abundance in SBRs (13.7 ∼ 16.1 %) than in CFRs (0.2 ∼ 0.3 %), aligning with its typical r-strategist characteristics. Methanothrix showed a higher enrichment in CFRs (33.1 ∼ 39.6 %) compared to SBRs (26.8 ∼ 29.9 %) at the same SRT, indicating K-strategist behavior. The SBRs had the potential to co-enrich both types of methanogens. Feeding regimes played a more pivotal role in the distribution of methanogens than SRT. The dominant bacteria, such as Desulfococcus and Mesotoga, as well as the archaeon Methanothrix, were auxotrophic in some essential amino acids, implying potential cross-feeding interactions. This study provides key insights into ecological strategies by linking microbiology with environmental technologies to enrich target methanogenic communities and enhance methane production.This study was supported by the Taighde Éireann – Research Ireland (formerly Science Foundation Ireland) and the Sustainable Energy Authority of Ireland under the SFI Frontiers for the Future Awards Programme (22/FFP-A/10346). Huanhuan Chang thanks the scholarship from the China Scholarship Council (No. 202208620052). Guangxue Wu thanks the support from the Galway University Foundation

Peddling stories: an investigation of the day‑to‑day realities for cyclists in Galway

Towns and cities worldwide are challenging the dominance of private cars and seeking ways to limit their use, compelled by the urgency to deal with the climate crisis. There is a growing determination to transition to more active and sustainable transport such as walking, cycling, and public transport, and to reduce our overall need for private car use in urban areas while improving public health and local environs. Cycling is a low-impact aerobic exercise and mobility option that can assist attempts to reduce private car use, cut harmful emissions, and moderate economically damaging traffic congestion in towns and cities. Cycling also offers numerous health and well-being benefits. But in towns and cities where cycling is constrained, marginalised, or ignored by transport planners, little is known about who is cycling, for what purpose, where they are cycling to and from, and their motivation to cycle. In recent times efforts have been made to improve the cycling infrastructure and better promote this mode of travel in Galway, a small city on the West coast of Ireland. This study investigates the experiences of cyclists in the city with data collected from individuals of differing ages and cycling abilities and advocates cycling continues to be marginalised and neglected in the context of implementing transport policy in the city. The findings indicate that most cyclists feel unsafe, and a prevailing car-centric policy mindset prevents cycling from developing to its potential in Galway to the detriment of the local environment, citizens' health, and new economic opportunities

MesenmiR: Towards a microRNA-modified mesenchymal stromal cell therapeutic for chronic limb-threatening ischaemia

Chronic limb-threatening ischaemia is the most severe manifestation of peripheral artery disease and is defined by the presence of ischaemic rest pain, ulceration, or gangrene of the lower limb. Despite current treatment options, many patients have no option but to undergo amputation. In chronic limb-threatening ischaemia, vascular occlusion causes distal tissue ischaemia, which results in a severe skeletal muscle pathology which is characterised by muscle atrophy, impaired satellite cell function, fibrosis, myosteatosis, mitochondrial dysfunction, and other features. There has been significant interest in novel therapeutics, such as cell and gene therapies, to improve outcomes in these patients. However, despite early promising data, translational success has been limited. These novel therapeutics have largely focussed on "therapeutic angiogenesis"; however, more recently, there has been increasing interest in targeting the skeletal muscle. MicroRNAs are small, non-coding RNAs that have also gained attention as pathological mediators and potential therapeutics in chronic limb-threatening ischaemia. In this thesis, I aimed to investigate skeletal muscle pathology, with a particular focus on its associated microRNA dysregulation, to inform the rational design of novel microRNA- and cell-based therapeutics. In Chapter 3, I utilised publicly available RNA sequencing data from gastrocnemius biopsies from chronic limb-threatening ischaemia patients to perform microRNA-Target Enrichment Analyses and identified a panel of microRNAs with a potential regulatory role in chronic limb-threatening ischaemia-associated skeletal muscle pathology. In a preclinical murine hindlimb ischaemia model, I showed that three of these microRNAs, miR-1, miR-133a, and miR-29b, were downregulated in skeletal muscle ischaemia on day 7 post-hindlimb ischaemia. Through functional enrichment analysis, I showed a role of this three-microRNA panel in regulating targets associated with fibrosis in the ischaemic limb. In chapter 4, I optimised a pre-existing protocol to isolate RNA from formalin-fixed paraffin-embedded skeletal muscle sections to profile the expression of microRNAs and mRNAs. I investigated the three-microRNA panel in human umbilical cord mesenchymal stromal cell-treated skeletal muscle in the hindlimb ischaemia model and further validated their downregulation at day 28 post-hindlimb ischaemia. Further, human umbilical cord mesenchymal stromal cell treatment was associated with a partial but significant restoration of miR-133a levels towards non-ischaemic levels. miR-133a levels were strongly correlated with decreased ischaemic damage of skeletal muscle in the hindlimb. In chapter 5, I established a 2D in vitro NIH-3T3 fibroblast and C2C12 myoblast coculture model with markedly enhanced myogenesis. In this model, induction of simulated ischaemia partially but inconsistently recapitulated features of skeletal muscle pathology seen in human chronic limb-threatening ischaemia and the murine hindlimb ischaemia model, such as myotube atrophy. However, further optimisation of this model will be required to enhance its applicability to chronic limb-threatening ischaemia research. Overall, the results in this thesis highlight the role of miR-1, miR-133a, and miR-29b in chronic limb-threatening ischaemia-associated skeletal muscle fibrosis and further the potential therapeutic relevance of restoration of miR-133a by human umbilical cord mesenchymal stromal cell-treatment in skeletal muscle ischaemia. It is hoped that further work on the lines of investigation opened in this thesis will facilitate the development of novel microRNA- and cell-based therapeutics aimed at restoring homeostasis in the ischaemic limb and, ultimately, improving outcomes in patients living with chronic limb-threatening ischaemia

Advance in planning and treatment of patients with complex coronary artery disease: From invasive coronary angiography to coronary computed tomography angiography and machine learning

[No abstract available

Ekphrasis spioradálta don Aos Óg: Alt léirmheasa ar Iontas na nIontas le Gabriel Rosenstock

Is teach foilsitheoireachta é Foilseacháin Ábhair Spioradálta a bunaíodh sa bhliain 1956 ‘faoi choimirce Ord na nÍosánach, chun leabhair a sholáthar i nGaeilge ar ábhair a bhaineann le creideamh agus leis an gcultúr Críostaí’ (2025) agus is cinnte go dtagann ábhar an ailt léirmheasa seo – Iontas na nIontas – leis na haidhmeanna sin. Cnuasach de véarsaí nua-chumtha is ea é don aos óg leis an scríbhneoir bisiúil, Gabriel Rosenstock, scríbhneoir a bhfuil aitheantas idirnáisiúnta bainte amach aige dá shaothar turgnamhach filíochta agus próis do pháistí agus do dhaoine fásta araon. Is cnuasach suaithinseach é Iontas na nIontas toisc na véarsaí ann a bheith curtha i láthair in éineacht le foinsí a n-inspioráide – píosaí ealaíne ón bhFearann Poiblí. ‘Ekphrasis’ a thugann Rosenstock (2024: 13) ar an stíl atá á cleachtadh aige sa chnuasach agus ar an ábhar sin, sa léirmheas seo, tabharfar sainmhíniú ar dtús ar an téarma ekphrasis agus luafar cuid de na ceisteanna eiticiúla is gá do chriticeoirí liteartha dul i ngleic leo agus ekphrasis á mheas acu. Féachfar ansin ar an tslí a gcuireann Rosenstock ekphrasis i láthair an léitheora óig in Iontas na nIontas agus léireofar gur freagairt spioradálta ar an ealaín é saothar Rosenstock atá ag iarraidh freagairt spioradálta a spreagadh sa léitheoir óg

The development, deployment, and analysis of a bespoke patient-family clinical videoconferencing system in Ireland during the COVID-19 pandemic

The Covid-19 pandemic accelerated digital transformation in healthcare worldwide. This period brought innovative telemedicine solutions and new challenges to healthcare systems. This MD thesis examines telemedicine in Ireland during the Covid pandemic and challenges encountered during this time. This research describes the rapid development and deployment of a bespoke video-conferencing platform, ICU FamilyLink, which was used to facilitate patient-to-family communication during the COVID-19 pandemic. This research identifies key technology requirements and the useability factors of video-conferencing systems in a healthcare setting and highlights the need for robust clinical cyber-resilience in the Irish healthcare system

Game engine based synthetic data generation schemes and convolutional neural networks

This thesis presents designs and implementations to generate synthetic images using game engines to train CNNs (Convolutional Neural Networks). It also investigates some fundamental properties of CNNs, namely the performance characteristics with different number of target classes; and the training characteristics using our novel learning rate tuning method. To train CNNs for a computer vision problem requires a huge number of annotated images as training data, which is labour intensive and expensive. A part or whole of the training data can be synthesized with a wide variety of methods. Our first contribution is to synthesize aerial top-down images and thereby, attempt and demonstrate the feasibility of two domain transfers at once, one being synth-to-real (training on synthetic data and predicting on real data), two being front-facing to aerial domain (taking a CNN pretrained on consumer camera images which are primarily front-facing and finetuning/testing that CNN on aerial top-down images). We generated synthetic data for that from a realistic virtual 3D game environment by programmatically flying a (quadrocopter) Robotic Aerial Vehicle (RAV) inside the game and annotating the synthetic images so taken from its camera. We then demonstrated dual domain transfer by detecting aerial-view real-world objects using a CNN trained on our synthetic data. Our second contribution is the design, development and evaluation of a hybrid synthetic data generation approach that combines the realistic lighting, object placements etc. from the 3D game engine with complex textures and backgrounds sourced from the internet. The network finetuned with synthetic data so collected outperforms the same network finetuned with real data when tested on a challenging dataset called ObjectNet and also sets a state-of-the-art result for any convolutional neural network on ObjectNet. Our third contribution is an investigative work that delves into performance characteristics of CNNs with increasing number of classes to predict. To that end, we conduct a systematic investigation on three ubiquitous computer vision tasks – image classification, object detection, and semantic segmentation, examining how performance changes with increasing number of class labels, while controlling for variables like CNN architecture and training methodology. We use multiple datasets for each task. We find that in image classification and semantic segmentation, performance decreases with increasing number of classes. Conversely, we discover that performance improves with more classes in object detection. We further explore this observed difference by visualizing and analyzing feature maps in terms of their clustering performance. We conclude that in object detection, the feature map clusters become tighter and better separated as the number of classes increases, leading to an increase in performance.1. European Union’s Horizon 2020 Research and Innovation Programme–Grant Agreement Number 700264 (ROCSAFE); 2. Prof. Michael Madden (PI Research Overheads) 3. . Science Foundation Ireland under Grant Number 12/RC/2289_P2–Insight SFI Centre for Data Analytics (co-funded by the European Regional Development Fund)