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
A diffusion model approach to forecast multi-sector demand growth for green hydrogen generated from offshore wind power
(Elsevier Ltd., 2025-07-11) Dinh, Quang Vu; Dinh, Van Nguyen; Leahy, Paul G.; Research Ireland; H-Wind academic-industry consortium; DP Energy Ltd; ESB; Equinor; Gas Networks Ireland; OWC; ABL
Green hydrogen, generated from renewable electricity, offers a means of decarbonising energy demand currently met by liquid and gaseous fossil fuels. However, this will require the roll-out of new technologies in diverse and distributed energy demand sectors. Therefore, the growth of hydrogen demand will depend on the rate of technological uptake in each sector. A bottom-up analytical method based on the Bass diffusion model is used to predict future hydrogen demand from the industrial, transport, power generation, and residential sectors. This is coupled with a dynamic model of green hydrogen supply based on offshore wind energy. Three scenarios for hydrogen demand in Ireland are developed and analysed as a case study. By 2050, Ireland's annual hydrogen demand is projected to reach approximately 500,000 tonnes, 950,000 tonnes, and 1,480,000 tonnes under low, medium, and high demand scenarios, respectively. To meet this demand solely through offshore wind-powered electrolysis, respective wind capacities of 5.2 GW, 9.8 GW, and 15.2 GW would be required. Future hydrogen demand is expected to mainly come from industry and zero-carbon power generation. The levelised cost of hydrogen will decline significantly as the first 500 MW of wind capacity is installed, driven by increased production scale. A sharp increase in hydrogen demand is predicted after 2030, highlighting the importance of timely infrastructure development and supportive policy frameworks. This approach highlights the gap between policy ambitions and the pace of technology diffusion, showing the potential to develop new large-scale demand sectors such as production of ammonia and sustainable aviation fuel.
Bridging the adoption gap for cryptocurrencies: understanding the affordances that impact approach–avoidance behavior for potential users and continuation usage for actual users
(Emerald Publishing Ltd., 2025-01-08) Armani Dehghani, Milad; Karavidas; Rese, Alexandra; Acikgoz, Fulya
Purpose: With the rise of cryptocurrency and its influence on the financial industry, this paper aims to explore cryptocurrency affordances that lead to approach–avoidance behavioral intentions for non-users (potential) and the intention to continue use for users (actual), drawing upon affordance theory and chasm theory. Design/methodology/approach: The authors collected data from 480 potential and actual users in Germany and used maximum likelihood structural equation modeling (ML-SEM) to analyze it. In particular, the data consisted of 301 cryptocurrency users in Germany\ the authors used ML-SEM to test the post-adoption model. Additionally, logistic regression was utilized to determine the dominant actual usage method (store of value or medium of exchange) for various cryptocurrency coins. Findings: According to the study's results, the perceived value benefits have a positive impact on the behavioral intention of potential users to adopt cryptocurrency, and they influence the intention of actual users to continue using it. However, both perceived volatility and financial risk tolerance are the most crucial factors hindering cryptocurrency adoption, whether in the pre-adoption or the post-adoption stage. Originality/value: This is the first study to reveal cryptocurrency affordances and examine their effect on behavioral intentions toward cryptocurrency adoption based on the differences between non-users (potential) and users (actual). Furthermore, the authors explore how cryptocurrency holders perceive and invest in different coins (e.g. NFTs), which sheds light on factors such as financial risk tolerance that affect their decision making.
Comparative performance evaluation of 5G-TSN applications in indoor factory environments
(Institute of Electrical and Electronics Engineers (IEEE), 2025-05-09) Zanbouri, Kouros; Noor-A-Rahim, Md.; Pesch, Dirk; Research Ireland
While Time-Sensitive Networking (TSN) enhances the determinism, real-time capabilities, and reliability of Eth-ernet, future industrial networks will not only use wired but increasingly wireless communications. Wireless networks enable mobility, have lower costs, and are easier to deploy. However, for many industrial applications, wired connections remain the preferred choice, particularly those requiring strict latency bounds and ultra-reliable data flows, such as for controlling machinery or managing power electronics. The emergence of SG, with its Ultra-Reliable Low-Latency Communication (URLLC) promises to enable high data rates, ultra-low latency, and minimal jitter, presenting a new opportunity for wireless in-dustrial networks. However, as 5G networks include wired links from the base station towards the core network, a combination of 5G with time-sensitive networking is needed to guarantee stringent QoS requirements. In this paper, we evaluate SG-TSN performance for different indoor factory applications and environments through simulations. Our findings demonstrate that 5G- TSN can address latency-sensitive scenarios in indoor factory environments.
Scalability analysis of 5G-TSN applications in indoor factory settings
(Institute of Electrical and Electronics Engineers (IEEE), 2025-05-09) Zanbouri, Kouros; Noor-A-Rahim, Md.; Pesch, Dirk; Research Ireland
While technologies such as Time-Sensitive Networking (TSN) improve deterministic behaviour, real-time functionality, and robustness of Ethernet, future industrial networks aim to be increasingly wireless. While wireless networks facilitate mobility, reduce cost, and simplify deployment, they do not always provide stringent latency constraints and highly dependable data transmission as required by many manufacturing systems. The advent of 5G, with its Ultra-Reliable Low-Latency Communication (URLLC) capabilities, offers potential for wireless industrial networks. 5G offers elevated data throughput, very low latency, and negligible jitter. As 5G networks typically include wired connections from the base station to the core network, integration of 5G with time-sensitive networking is essential to provide rigorous QoS standards. This paper assesses the scalability of 5G-TSN for various indoor factory applications and conditions using OMNET++ simulation. Our research shows that 5G-TSN has the potential to provide bounded delay for latency-sensitive applications in scalable indoor factory settings.
Combining mucosal microbiome and host multi-omics data shows prognostic potential in paediatric ulcerative colitis
(Nature Publishing Group, 2025) Kulecka, Maria; O'Sullivan, Jill; Fitzgerald, Rachel; Velikonja, Ana; Huseyin, Chloe E.; Laserna-Mendieta, Emilio J.; Ruiz-Limón, Patricia; Eckenberger, Julia; Vidal-Marin, Miriam; Truppel, Bastian-Alexander; Singh, Raminder; Naik, Sandhia; Croft, Nicholas M.; Temko, Andriy; Zomer, Aldert; Melgar, Silvia; Deb, Protima; Sanderson, Ian R.; Claesson, Marcus J.; Science Foundation Ireland; Crohn’s and Colitis Foundation Litwin IBD Pioneers program; European Commission; Instituto de Salud Carlos III
Current first-line treatments of paediatric ulcerative colitis (UC) maintain 6-month remission in only half of the patients. Relapse-prediction at diagnosis could enable earlier introduction of immunosuppressants. We collected intestinal biopsies from 56 treatment-naïve children, combining mucosal quantitative microbial profiling with host epigenomics, transcriptomics and genotyping, and in vitro and in vivo experiments on selected bacteria. Baseline bacterial diversity was lower in relapsing children, who had fewer butyrate-producers but more oral-associated bacteria, whereof Veilonella parvula induced inflammation in epithelial cell lines and IL10-/- mice. Microbiota had the strongest association with future relapse, followed by epigenome and transcriptome. Relapse-prediction from separate omics data was outperformed by a robust machine learning approach combining microbiomes and epigenomes. In summary, combinatory machine learning of host-microbe data, especially microbiome and epigenome, had prognostic potential in paediatric UC. Our translational findings also suggest that pro-inflammatory oral-associated colonizers can exploit reduced colonic bacterial diversity of relapsing children.