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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A smart energy service for the commercial rented sector: decision making points and business model considering the learnings from the pilot sites of the SmartSPIN project in Ireland, Spain and Greece
(Open Research Europe, 2024) De Tommasi, Luciano; Agrawal, Ruchi; Lyons, Padraig
This paper presents an evaluation of the smart energy service for the commercial rented sector developed during the European Commission’s Horizon 2020 funded research project SmartSPIN, highlighting the key decision-making points in relation to energy performance contracting in a rented commercial property. The perspectives of building owner, energy service company (energy service provider) and renters, are examined using a quantitative model that enables to evaluate the Net-Present-Value of the energy efficiency project for each party, considering the contract duration, energy cost savings, energy prices, investment costs and operation and maintenance costs, thereby highlighting the variables that determine the decision-making leading to the contractual agreements for the smart energy service. Furthermore, the deployment of the business model for the proposed smart energy service is analysed by means of the CANVAS table, which enables to determine how to create value for the ESCO businesses, covering the following aspects: key partners, key activities, key resources, cost structure, value proposition, customer relationship, channels, customer segment, revenue streams. The proposed smart energy service has been implemented and evaluated by considering the experiences of the stakeholders and the feedback that the pilot participants of the SmartSPIN project in Ireland, Greece and Spain provided during the demonstration activities. This process enabled to identify improvements which eventually led to the business model validation. Finally, leveraging the experience gained from activities at the SmartSPIN pilot sites and engagement with the key stakeholders, this paper also further elaborates on how to create value for the customers using the Value Proposition CANVAS template.
A constraint-based parallel local search for the edge-disjoint rooted distance-constrained minimum spanning tree problem
(Springer Nature, 2017-06-16) Arbelaez, Alejandro; Mehta, Deepak; O’Sullivan, Barry; Quesada, Luis
Many network design problems arising in areas as diverse as VLSI circuit design, QoS routing, traffic engineering, and computational sustainability require clients to be connected to a facility under path-length constraints and budget limits. These problems can be seen as instances of the rooted distance-constrained minimum spanning-tree problem (RDCMST), which is NP-hard. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to ensure that all clients are connected to two or more facilities via edge-disjoint paths. We call this problem the edge-disjoint RDCMST (ERDCMST). Previous work on the RDCMST has focused on dedicated algorithms and therefore it is difficult to use these algorithms to tackle the ERDCMST. We present a constraint-based parallel local search algorithm for solving the ERDCMST. Traditional ways of extending a sequential algorithm to run in parallel perform either portfolio-based search in parallel or parallel neighbourhood search. Instead, we exploit the semantics of the constraints of the problem to perform multiple moves in parallel by ensuring that they are mutually independent. The ideas presented in this paper are general and can be adapted to other problems as well. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances taken from real-world passive optical network deployments in Ireland, Italy, and the UK. Our results show that performing moves in parallel can significantly reduce the elapsed time and improve the quality of the solutions of our local search approach.
Visualising the catalogues of digital editions
(Michigan Publishing, 2024-09-19) Kurzmeier, Michael; O'Sullivan, James; Pidd, Mike; Murphy, Orla; Wessels, Bridgette; Irish Research Council; Arts and Humanities Research Council
This article provides a data-driven overview of the developments in the field of digital scholarly editing. It surveys and evaluates the available data source on digital scholarly editions and provides longitudinal analysis of changes in number of projects, geographic distribution, licensing, interfaces and preservation. Digital scholarly editions (DSE) are essential to arts and humanities research, but also, society and culture at large. They are the primary instrument through which textual and cultural heritage, expert knowledge, and public understanding are negotiated. Their comparatively long history makes them especially suited for a diachronic approach, describing their change over time. While digital editions can vary greatly in scope and lifespan, a quantitative analysis of two of the most comprehensive data sources on digital editions can produce data-based insight into the developments within the field over time. Exploring this history and at the same time assessing the available metadata on DSEs is the aim of this article. It presents the state of the two most comprehensive available sources on digital editions and details the methodology and visualisation process undertaken. In its analysis, it is at the same time a quantitative approach to DSEs as well as a critique of the available data sources on editions.
(Post-)pandemic somatechnics, neoliberalism, and the return to (academic) normalcy: Digital conversations
(Edinburgh University Press, 2024-11) Rahbari, Ladan; Geerts, Evelien
This essay consists of a set of digital (post-)pandemic email correspondence held between a political sociologist and an interdisciplinary philosopher working at western European universities while the COVID-19 pandemic rapidly unfolded itself. Starting from an unsettling point in time in 2021, during which vaccination strategies and numerous eugenic pandemic containment measures were being discussed, the authors touch upon issues as diverse as the importance of embodied feminist theorising in pandemic crisis times; neoliberal extractive capitalism’s influence on society, pandemic (mis)management, and higher education; the problematic (post-)pandemic business-as-usual-narrative; grief, mourning, and trauma; the power of anger and protesting; and the forced return to normal(cy). These conversations are held together by an irruptions-based methodology based on Deleuze and Guattari (2000) . This methodology tries to make sense of the (post-)pandemic as a disruptive event while forming the backdrop for conversational and critical theoretical snippets, self-designed memes, and critical race, queer, disability, and feminist theoretical perspectives that all conceptualise (post-)pandemic somatechnics as a ‘form of ethico-political critical practice’ (Sullivan and Murray 2011 : vii).
A digital twin of intelligent robotic grasping based on single-loop-optimized differentiable architecture search and sim-real collaborative learning
(Springer Nature, 2024-10-14) Jiao, Qing; Hu, Weifei; Hao, Guangbo; Cheng, Jin; Peng, Xiang; Liu, Zhenyu; Tan, Jianrong; National Natural Science Foundation of China; Key Research and Development Program of Zhejiang Province; Natural Science Foundation of Zhejiang Province
The effectiveness of deep learning models for vision-based intelligent robotic grasping (IRG) tasks typically hinges upon the deep neural network (DNN) architecture as well as the task-oriented annotated training samples. Nevertheless, current methods applied for designing DNN architectures depend on human expertise or discrete search by evolution and reinforcement learning algorithms, which leads to enormous computational cost. Moreover, DNNs trained solely on simulation-labeled data face challenges in direct real-world deployment. In response to these concerns, this paper proposes a new stable and fast differentiable architecture search method (DARTS) based on a single-loop optimization framework, named single-loop-optimized DARTS (SLO-DARTS). This method enables simultaneous updates to the weights and architecture parameters of neural networks by continuously relaxing the discrete search space. Additionally, a digital twin (DT) framework integrating the Grasp-CycleGAN method is developed to minimize the visual gap between simulated and real-world IRG scenarios, enhancing the transferability of DNNs trained in simulation. The DT framework can not only enhance the IRG accuracy but also save the costly expense of large-scale real labeled data collection. Experiments demonstrate that the proposed SLO-DARTS method achieves a time-efficient optimization process while delivering a DNN with improved prediction accuracy compared to the original dual-loop-optimized DARTS method. The developed DT framework produces IRG accuracies of 92.6%, 86.3%, and 83.7% for single household objects, single adversarial objects, and cluttered objects, respectively.