CORA
Cork Open Research Archive (CORA) is UCC’s Open Access institutional repository which enables UCC researchers to make their research outputs freely available and accessible.
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Recent Submissions
Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data
(Social Science Research Network (SSRN), 2024-09-18) Dokur, Emrah; Erdogan, Nuh; Sengor, Ibrahim; Yüzgeç, Uğur; Hayes, Barry P.
The widespread adoption of low carbon technologies (LCTs) such as photovoltaics, electric vehicles, heat pumps, and energy storage units introduces challenges to distribution network congestion and power quality, particularly raising concerns about voltage stability. Enhanced voltage visibility in low-voltage (LV) networks is increasingly vital for an active grid management, making efficient voltage forecasting tools essential. This study introduces a novel data-driven approach for forecasting node voltages in LCT-enriched LV distribution networks. Using time series of power measurements from smart meter data, the study integrates an Extreme Learning Machine (ELM) with the Single Candidate Optimizer (SCO) to enhance both computational efficiency and forecasting accuracy. The model is validated using a realistic LCT-enriched LV network dataset and benchmarked against several established machine learning models. Results demonstrate that the SCO algorithm effectively optimizes ELM parameters, achieving up to a 17-fold reduction in computation time compared to the fastest metaheuristic methods implemented. The proposed model demonstrated superior accuracy, with an average voltage deviation of 0.56%. While the computation time per node achieved is not yet suitable for real-time applications, the study proves that SCO significantly enhances ELM performance.
Introducing outcome-based education in obstetrics and gynaecology training: Perspectives of trainees and trainers
(Elsevier Ltd., 2025-01-08) O’Sullivan, Orfhlaith E.; Leitao, Sara; S. Harney; M.E. Abdalla; O’Donoghue, Keelin
Background and aims: Outcome-based education (OBE) focuses on clearly defined learner goals, offering a structured framework to achieve competency. This study explores the perspectives of trainees and trainers in Obstetrics and Gynaecology (O&G) in Ireland regarding facilitators, barriers, and challenges to implementing OBE. Methods: A national cross-sectional survey was distributed to O&G trainees and trainers in Ireland. Responses were analysed using descriptive statistics and chi-squared tests, and qualitative thematic analysis. Results: A total of 151 trainees and trainers participated in the study. While 61.2% of respondents reported familiarity with the concept of OBE, only 22.4% accurately identified its primary focus on learner goals. Participants highlighted several key benefits of OBE, including the establishment of clearly defined goals and the development of competency in essential skills. However, significant challenges were also identified, such as the perception of unattainable goals for trainees and insufficient training facilities. Additionally, trainer engagement and the lack of allocated time for both trainers and trainees to attend training courses were recognized as major barriers to the successful implementation of OBE. Conclusion: OBE presents a promising educational framework for O&G training, with the potential to modernize and enhance learning outcomes. However, its successful implementation hinges on comprehensive education about its principles and benefits, substantial investment in educational facilities and resources, and the prioritization of training through dedicated and protected time for both trainees and trainers.
A heuristic method for perishable inventory management under non-stationary demand
(Elsevier Ltd., 2025-01-07) Gulecyuz, Suheyl; O’Sullivan, Barry; Tarim, S. Armagan; Science Foundation Ireland
Our study considers a perishable inventory system under a finite planning horizon, periodic review, non-stationary stochastic demand, zero lead time, FIFO (first in, first out) issuing policy, and a fixed shelf life. The inventory system has a fixed setup cost and linear ordering, holding, penalty, and outdating costs per item. We introduce a computationally-efficient heuristic which formulates the problem as a network graph, and then calculates the shortest path in a recursive way and by keeping the average total cost per period at minimum. The heuristic firstly determines the replenishment periods and cycles using the deterministic-equivalent shortest path approach. Taking the replenishment plan constructed in the first step as an input, it calculates the order quantities with respect to the observed inventory states as a second step. We conduct numerical experiments for various scenarios and parameters, and compare them to the optimal stochastic dynamic programming (SDP) results. Our experiments conclude that the computation time is reduced significantly, and the average optimality gap between the expected total cost and the optimal cost is 1.87%.
Is atuirseach cásmhar ró-chathach le spás me
(National University of Ireland, 2016) Ní Íceadha, Máire; Mac Mathúna, Liam
Soláthraítear anseo eagrán den amhrán molta mná ‘Is atuirseach cásmhar ró-chathach le spás me’ atá curtha i leith an fhile Eoghan Rua Ó Súilleabháin (1748–84) i lámhscríbhinn dhéanach. Beartaíodh ar eagrán den amhrán a sholáthar ar an mbonn gur amhrán neamhfhoilsithe de chuid Eoghain Rua is ea é. Níor thángthas ach ar aon chóip amháin den amhrán sna lámhscríbhinní. Tá sí le fáil in ARÉ 23 O 74 (1383) 1, i lámhscríbhinn a scríobh an scríobhaí Seán Baróid sa bhliain 1816. Ba as Ceann Toirc, i gCo. Chorcaí, don Bharóideach.1 Scríobh sé ARÉ 23 O 74 (1383) le linn dó a bheith i bPríosún Chorcaí i gcaitheamh an tsamhraidh agus an fhómhair 1816 de réir na gcuntas air.2 Is í ARÉ 23 O 74 (1383) an t-aon lámhscríbhinn atá againn óna láimh.3 Tá dearmad amháin léirithe i leagan amach na cóipe ar an gcéad leathanach (ARÉ 23 O 74 (1383) 1). Tá an chuma air gur fhág an Baróideach l. 29 den amhrán ar lár ar dtúis ach gur thug sé faoi deara é.Scríobh sé an líne ar thaobh an leathanaigh chéanna (ARÉ 23 O 74 (1383) 1) le comhartha don léitheoir go raibh an líne úd le cur isteach tar éis l. 28 den amhrán.
Radiographer/radiologist education and learning in artificial intelligence (REAL-AI)
(ROC Events, 2023) Doherty, Geraldine; McLaughlin, Laura; Rainey, Clare; Hughes, Ciara; Bond, Raymond; McConnell, Jonathan; McFadden, Sonyia L.
Background: Artificial intelligence (AI) is incipient in radiography, and whilst there are many studies investigating its potential in the clinical environment, there is a paucity of research investigating the needs of clinical staff. Further research is required to identify what training and preparation is required for a new AI-powered work environment, or indeed what AI education is available at undergraduate and postgraduate levels.
Method: This CoRIPS funded study included two electronic surveys (i) one was performed amongst radiographers and radiologists investigating their baseline AI knowledge, identifying what training they desire and preferred method of delivery. (ii) the second survey was for academics and educators in Higher Education Institutions to identify educational provision of AI in the radiography curriculum across the UK and Europe.
Method: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. Participant feedback will determine perceptions of clinical staff and identify topics for inclusion in postgraduate/undergraduate programmes.
Method: will inform the next phase of the study which will incorporate focus groups with staff to explore adaptation of the curricula to enable incorporation of AI into clinical practice.
Conclusion: Radiographers, radiologists and Higher Education Institutions have been surveyed to ascertain current knowledge and needs for AI training. Collaboration and symbiosis between academia, clinical and industry partners is possible, to pioneer AI education tailored to medical imaging staff. The impact of this research has the potential to be of significant value across disciplines within the wider healthcare sector.