Digital Commons Network^™

Information Manager (Bim) Role Profiles, Davitt Lamon, Robert Moore, Stephen Lynam

Reports

This document, Information Manager (BIM) Role Profiles, has been prepared in support of the Irish Government’s Building Information Modelling (BIM) Mandate. It is the result of a collaborative effort between Build Digital, the Department of Public Expenditure, Infrastructure, Public Service Reform and Digitalisation (DPER), the Infrastructure Division, and the National Standards Authority of Ireland (NSAI). Its primary purpose is to define the competencies, responsibilities, and experience required for Information Management (BIM) roles to facilitate the successful digital delivery of public works projects. It is important to recognise that under the ISO 19650 series of standards, Information Management is defined as …

Sustainability Tool Inventory, Robert Moore, James O'Donnell

Tools

The purpose of this inventory is to inform Irish organisations, including the private sector, public agencies and semi-states, about Sustainability Tool

Holographic Photopolymerizable Glass: Enhancing Exposure Sensitivity By Tailoring Photoinitiating Systems Based On Bodipy Dyes, Tatsiana Mikulchyk, Aimee Sheehan, Metodej Dvoracek, Izabela Naydenova, Mikhail Filatov

Research Outputs: 2025-Present

The photoinitiating system (PIS) is a key component of holographic materials which needs to be carefully selected and optimized to achieve efficient photopolymerization and, as a result, highly efficient holographic structures. Recently, boron dipyrromethene (BODIPY) dyes have been introduced as a new class of photosensitizers for holographic materials, including photopolymerizable glass, which is characterized by excellent environmental stability. Here, we present a study of the holographic recording capability of the photopolymerizable glass, where PIS was optimized using two approaches: 1) adding a second co-initiator, diphenyl iodonium hexafluorophosphate (DPIHFP) and 2) exploring a range of BODIPY structures, such as anthracene- and …

Building Services Engineering January/February 2026

Building Services Engineering

No abstract provided.

Heterocyclic‐Fused Squaramides: Anionophores With Antimicrobial Activity, Luke Edward Brennan, Chris Samuel Hawes, Kevin Kavanagh, Robert Brian Peter Elmes

Research Outputs: 2025-Present

Antimicrobial resistance is regarded as one of the foremost public health risks of the 21st century, highlighting the need for new antimicrobial agents and chemical tools to interrogate their biological activity. Artificial transmembrane anion transporters have emerged as promising supramolecular scaffolds in this context, having demonstrated membrane activity in mammalian systems and showing growing potential in antimicrobial research. Here, we report a series of heterocycle‐fused squaramide anionophores capable of binding and transporting chloride. Several members of this series display measurable antimicrobial activity, alongside evidence of intracellular chloride modulation in bacterial cells. Taken together, these findings indicate that membrane localisation and …

How Art You Today? Art, Aesthetics And Creativity In Social Educational Care Work Curricula Under Pressure., Bart Vanhoenacker

Journal of Social Care

Curricula in Social Educational Care Work increasingly face structural pressures shaped by neoliberal logics, managerial priorities and cost-efficiency paradigms. Creative and artistic components, traditionally central in the curricula, are particularly targeted, despite a growing consensus in research and professional practice regarding the indispensable role of art and creativity for personal development, well-being, social connection and inclusion. This article analyses institutional pressures, shifting curriculum priorities and the risks associated with diminished artistic courses and outlines pathways for strengthening art‑based and creative pedagogies in the current challenging context.

Les programmes d'études en travail social éducatif sont soumis à une pression structurelle croissante, …

The Ripple Effect: How The Hse Data Breach Shaped Public Opinion, Mugdha Srivastava, Abhishek Kaushik, Roisin Loughran, Fergal Mccaffery

Women+ in Early Career Research Symposium

The digital world is rapidly evolving, and healthcare systems now depend heavily on data-driven and connected technologies. While these tools bring many benefits, they also increase exposure to data breaches and cyberattacks, raising concerns about privacy, security, and trust. Although earlier research has focused on technical and policy solutions for data protection, less attention has been paid to how major cyber incidents influence public attitudes. This study addresses that gap by examining how a major cyberattack relates to people’s views on data security and trust in digital health. We surveyed individuals about their personal cybersecurity practices and their opinions on …

Am I Walking Straight? Gait, The New Frontier Of Biometric Identification, Ruhi Anand Finn

Women+ in Early Career Research Symposium

Extended Reality (XR) is an umbrella term that integrates Virtual, Mixed and Augmented Realities. Each modality presents new forms of digital experiences, offering unique opportunities, benefits and risks. Additionally, newer iterations of XR leverage Artificial Intelligence (AI) to enhance numerous applications. This enhancemen is sustained through new levels and contexts of data capture, including continuous biometric data collection (gaze, face and gait) and incidental bystander capture. This paper investigates the implications of XR technologies on data privacy, and the need for considerations of collective social privacy. The paper argues that, given the very narrow definition and categorization of biometric data …

Beyond The Sayable: Wittgenstein, Theory Of Mind And Affective Simulation Of Llms, Haomiaomiao Wang, Lili Zhang, Tomás E. Ward

Women+ in Early Career Research Symposium

Wittgenstein’s distinction between what can be said and what can only be shown frames a limit of propositional language. Affect and aesthetic are not primarily matters of correct description but of how expressions function within shared forms of life. Large language models (LLMs), however, increasingly produce fluent language that resembles such understanding by reproducing the patterns through which people ordinarily talk about emotion and perspective.

This paper argues that apparent Theory of Mind (ToM) in LLMs is understood as competence in the publicly shared patterns of mental-state language, rather than as grounded understanding. Using abstract artworks, we show that LLMs …

Bridging Research And Practice: Your Guide To Implementing Evidence-Based Gender Equality Actions In Computing Education, Alina Berry

Women+ in Early Career Research Symposium

What if you could access proven strategies for promoting gender equality without spending months reviewing academic and grey literature? TechMate makes this possible. This free, open-access toolkit transforms scattered research into actionable guidance (actions) that anyone can use, whether you are a lecturer wanting to make your classroom more inclusive, a department head developing new policies, or a student advocate pushing for change. Actions are structured into four context-based categories and relevant subcategories. Each action comes with implementation instructions, where relevant, with ready-to-use templates, and links to the supporting evidence. While developed for computing higher education, TechMate’s research-driven approach is …

Engaging Older Adults Through Artefact-Driven Approaches For Ai Literacy Development: A Multi Stakeholder For Digital Inclusion, Paula Kelly, Mayank Parmar, Damon Berry, André Almo, Fatima Badmos, Peterson Jean

Women+ in Early Career Research Symposium

As Artificial Intelligence (AI) becomes deeply integrated into modern ICT and e-Accessibility services, older adults face a growing "digital divide" often characterised by a lack of awareness regarding how automated AI systems frequently functioning as "black boxes" infiltrate and impact their daily lives. This paper presents a multimodal workshop model designed to foster critical AI literacy among older adults, transitioning them from passive recipients to active stakeholders. Utilising an Action Research framework, the study details the co-creation phase of a three-phase national Age- Friendly AI literacy initiative in Ireland. Central to the methodology is the use of tangible exhibits as …

Lidar Mirror Detection Model For Autonomous Robotics, Sofiia Sarana

Women+ in Early Career Research Symposium

LiDAR sensors are widely used in autonomous robots and vehicles for mapping, navigation, and obstacle avoidance. Because mapping is a foundational capability in nearly all robotic systems, it must be both accurate and reliable. However, LiDAR frequently fails in environments containing mirrors or highly reflective surfaces, where distorted returns corrupt maps and generate false obstacles. Current solutions typically rely on additional sensors and computationally expensive sensor-fusion pipelines, which is costly and difficult to scale for compact robots with limited onboard resources. This paper presents a machine-learning–based LiDAR mirror detection model that identifies reflective surfaces directly from LiDAR data, reducing the …

When Irl Is Not An Option: Social Media Bans And The Exclusion Of Autistic Youth, Melanie Gruben, Ashley Sheil, Anne Ryan Duffy, Jacob Camillleri, Michelle O'Keeffe, Hazel Murray

Women+ in Early Career Research Symposium

After the roll-out of the social media ban in Australia in 2025, policy makers are considering psychosocial benefits and logistical implications. However, the needs of autistic people, who may rely more on the Internet for social connection earlier in life, should be taken into account. This white paper proposes a responsibility to support alternative means of social connection for autistic young people where bans are considered, as IRL (in real life) socialising is not always accessible.

Selfhealth: Continuous Semantic Intelligence For Privacy-Aware Lifelogging, Allie Tran

Women+ in Early Career Research Symposium

Lifelogging — the continuous digital recording of everyday experience through wearable cameras and sensors — has long promised benefits for memory augmentation, health monitoring, and lifestyle analysis. However, current systems follow a capture–store–retrieve model that delays analysis until after data collection, limiting their practical usefulness and creating significant privacy risks through prolonged retention of sensitive raw data. This paper argues that lifelogging should instead analyse data at the moment of capture, enabling timely, context-sensitive support and safer handling of personal information through privacy-by-design. We demonstrate the feasibility of this approach through SelfHealth, an early prototype system built on a custom …

A Human-Centred View Of Maritime Autonomous Surface Ships Operations Using The Shell Model, Natasya Habibah, Paul Hegarty, Breda Kenny

Women+ in Early Career Research Symposium

The concept of Maritime Autonomous Surface Ships (MASS) has existed for decades and is advancing rapidly as a result of developments in artificial intelligence. Despite these technological advancements, human involvement remains essential to ensure safety and reliability. This study applies the Software, Hardware, Environment, and Liveware (SHELL) model to develop a human-centric framework for examining the integration of human elements with intelligent and automated technologies in MASS. The framework considers software, with particular attention to regulatory aspects; hardware, including general system and sub-system architecture; environmental factors, including both natural and artificial, as well as dynamic and static elements; and liveware, …

What Educational Experiences Influence 2nd Level Students To Study Computer Science? - A Longitudinal Phd Study, Miriam Harte

Women+ in Early Career Research Symposium

Computing is an increasingly vital skill within the modern economy [3], prompting the Irish Department of Education to take action to integrate computing curricula across all stages of formal education. Despite the growing demand for computer scientists, a significant gap exists between the introduction of computing at 2nd level and students’ continuation in computing at Senior Cycle and 3rd Level education. We are aware that factors such as socioeconomic back- ground [2] and even teacher availability affect student engagement in Computer Science (CS) [1]. We intend to investigate the role of educational experiences in shaping 2nd Level students’ decisions to …

Comparative Time Series Forecasting Of Pedestrian Footfall In Dublin City Centre, Mercy Koech

Women+ in Early Career Research Symposium

Research on urban populations has turned out to be a significant field of study for a long duration.Forecasting pedestrian footfall is important for urban management,retail planning,crime analysis and evaluating the influence of public gatherings.With the rise of smart cities and the proliferation of sensor technologies there now exists a rich supply of data that offers unprecedented insight into the behaviour of pedestrain traffic.This study undertakes a comprehensive analysis of timeseries forecasting methods to predict pedestrian footfall trends in Dublin City Centre a key urban area with diverse spatial characteristics and high foot traffic variability.Using data collected via PYRO-BOX Counters sensors …

Letter From Editors, Andrea C. Heaney, Elizabeth Darnell

Women+ in Early Career Research Symposium

Letter from editors of 2026 Women+ Early Career Research Symposium

Feedback In Digital Game-Based Learning: A Taxonomy And The Design And Empirical Evaluation Of A Feedback System In A Mathematics Serious Game, André Almo

Dissertations

Digital Game-Based Learning (DGBL) is an active, student-centred pedagogical approach in which feedback plays a central role by informing learners’ actions, guiding decision-making and shaping motivation and engagement. Despite its importance, feedback in serious games is often described inconsistently and insufficiently in research, limiting comparability across studies and the accumulation of design knowledge, particularly for children. This thesis addresses these gaps through two complementary contributions: the development of a taxonomy for feedback design in digital serious games and the empirical evaluation of a taxonomy-informed feedback system in a mathematics game for primary school students. First, this work introduces the Taxonomy …

Shaping The Future Of Vertical Bone Augmentation: A Detailed Review Of Additive Manufacturing And 3d Biodegradable Scaffolds, Sleem Abdelstar Farag, Refaie Ahmed Omar, Salah A. Salman, Ahmed Barhoum, Gomaa A. Abdalla, Abdalla Abdal Hay Ali

Research Outputs: 2025-Present

Achieving effective alveolar bone augmentation in the vertical dimension presents a significant challenge in periodontal tissue regeneration, particularly for optimizing dental implant procedures. Sufficient alveolar bone height is crucial for successful implant placement. To achieve this, several surgical approaches—such as autologous bone block grafting, distraction osteogenesis, and guided bone regeneration (GBR)—are commonly used, often in combination with natural or synthetic grafting materials. However, these conventional methods face limitations, including morbidity, dimensional instability, structural weaknesses, handling difficulties, and high failure rates. Recent advancements in additive manufacturing, present innovative solutions by enabling the creation of 3D porous scaffolds with essential properties for …

Improvements In The Thermal Stability And Mechanical Robustness Of Holograms Recorded In Acrylamide/Polyvinyl Alcohol-Based Photopolymers Through The Use Of D-Sorbitol As A Plasticizer, Michael Murray, Aaron Byrne, Izabela Naydenova, Suzanne Martin

Research Outputs: 2025-Present

In this work, a photopolymer formulation is presented that demonstrates significant improvements in layer hardness and temperature stability, while retaining the excellent holographic recording characteristics and ease of preparation associated with acrylamide (AA)/polyvinyl alcohol (PVA)-based photopolymers. This is achieved through the use of N-phenyl glycine as an electron donor and, for the first time, D-Sorbitol, a naturally occurring sugar-alcohol, as a plasticizer. The plasticizing effect of D-Sorbitol is demonstrated through the minimal loss of diffraction efficiency (η) observed upon UV bleaching (fixing) of 500 lines/mm gratings, which is evidence for effective mass transport during holographic recording. This solves a key …

Temperature Dependence Of The Imaging Properties Of Holographic Lenses, Tomás Lloret, Jorge Lasarte, Suzanne Martin, Izabela Naydenova, Inmaculada Pascual

Research Outputs: 2025-Present

The demand for advanced photonic technologies to enhance augmented reality (AR) devices has led to a significant interest in new optical materials suitable for operation in sometimes challenging environmental conditions, such as elevated temperatures levels. Holographic optical lenses (HOLs), fabricated using holographic recording, offer several advantages over traditional refractive lenses, including greater versatility, lower cost, lower weight, and increased adaptability. In this study, we investigated the optical and thermal performance of HOLs recorded on four different photosensitive materials: Bayfol HX200, Bayfol HX120, a PVA/acrylamide-based photopolymer (AA/PVA), and a photopolymerisable hybrid sol–gel (PHSG). The lenses were exposed to thermal cycles 20 …

A Holistic Modelling Framework For Functionally Safe Software Architectures In Embedded Control Systems, Thomas Barth

Doctoral

Embedded control systems are integral to most modern electrified products and an essential backbone of ongoing digitalisation [1]. In this context, these systems increasingly perform safety-critical functions where failures can lead to severe personal injury, environmental damage, or significant economic loss [2]. Consequently, they fall more often within the scope of regulation such as IEC 61508 and its derivatives [3]. At the same time, driven by hardware evolution and market demands, embedded control systems continue to grow in both integration density and functional complexity [4]. These demands necessitate structured development methods that balance compliance with cost-efficiency and development agility. A …

On The Automatic Image Captioning Task In Italian: A Human-Centric Approach, Valentina De Amicis, Rajesh Jaiswal, Fernando Perez-Tellez

Research Outputs: 2025-Present

Automatic Image Captioning (AIC) combines two distinct machine learning disciplines, Computer Vision (CV) and Natural Language Processing (NLP), forming a challenging research landscape. Much research has been carried out for AIC in the English language facilitated by the availability of English language datasets. However, this is not true for other languages. The aim of this work is to investigate existing AIC models' compatibility for the Italian language. The scope of this research will be aimed to reduced complexity, improved efficiency, and readily trainable models on smaller datasets while maintaining an acceptable level of caption quality. The popular Show and Tell …

Translating Low-Level Features Into Student Friendly Explanations Using Xai, Ben Johnson, Keith Nolan

Research Outputs: 2025-Present

An alarming decline in Gaeilge proficiency has renewed education efforts, fueled by the rise of AI in recent years. This research presents the development of a deep learning framework designed to evaluate spoken Gaeilge (henceforth Irish) proficiency of learners by quantifying student voice sample similarity to everyday Irish speaker truth samples, combined with explainable AI (XAI) techniques used to generate proof-of-concept student-friendly feedback. While automatic speech recognition (ASR) systems for high-resource languages like English have access to large, detailed datasets, the Irish language relies on a limited availability of speech resources, that challenges traditional model development. We address these gaps …

An Explainable Multimodal Framework For Real-Time Bitcoin Forecasting, Dipesh Badal, Abdelsalam Busalim, Donghyeok Lee

Research Outputs: 2025-Present

High-frequency crypto forecasting requires systems that are accurate, explainable, and designed for human decision-making. Bitcoin presents a unique challenge for Human-Centred AI (HCAI) due to its volatility and sensitivity to heterogeneous technical, fundamental, and sentiment signals. This paper presents an explainable multimodal framework for Bitcoin forecasting at 15-minute resolution. We align five modalities - market data, on-chain metrics, the Fear & Greed Index (FGI), news, and Reddit - onto a unified, leakage-safe 15-minute grid. We evaluate tree-based, sequential, and Multimodal Fusion Block (MFB) models for next-interval log-return prediction using chronological splits. Results show that while short-horizon prediction remains challenging, multimodal …

On Explaining The Sentiments In Prediction Of Stock Movement: An Xai-Based Analysis, D. J. Ranade, Sarthak Bhaya, Siddhanth Bhimakari, Moe Aye Chan, Keith Quille, Rajesh Jaiswal

Research Outputs: 2025-Present

Empirical evidence shows that short-horizon equity returns are not fully random; there is a degree of predictability, especially when traditional financial data is combined with public sentiment measures. Sentiment-based features have been used to improve prediction tasks, but there is limited explanation of their contribution. In addition, the impact of news on investor sentiment, and how that impact decays over time, has rarely been investigated. To bridge this gap, we integrate sentiment scores extracted from financial news headlines using HKUST FinBERT with daily market-based indicators to predict the next-day price direction. We evaluated two aggregation strategies: the most confident news …

Data Preprocessing Methods For Automating Mlops Pipelines: A Comparative Study, Surya Teja Gowd Ayinavilli, Keith Quille, Tarry Singh

Research Outputs: 2025-Present

Preprocessing tools for data are increasingly being utilized in MLOps pipelines to develop models automatically. However, the fairness and reliability of automated processes are inadequately researched, risking causing performance degradation or bias. This discrepancy is addressed in this thesis with an evaluation of automated data preprocessing methods compared to a baseline approach, designed for integration into a TensorFlow Extended (TFX) pipeline. The performance of each method was compared in terms of classification measures and subgroup fairness to determine potential bias. Significance tests were employed to compare the performance of each automated method against the baseline. The results indicate that around …

Educators' Evaluation Of Explanation Types In Xai For Higher Education Dropout, Henry Maathuis, Jan Glazenborg, Meike Grol, Keith Quille

Research Outputs: 2025-Present

First-year student dropout rates represent a global challenge, particularly in Computer Science programs. In response, the Predict Student Success (PreSS) tool was developed over two decades using Naïve Bayes, a black box machine learning model, to identify students at risk of early withdrawal or academic failure. Given growing concerns about the use of black box models in high-stakes domains such as education, this study builds on former research by comparing the Naïve Bayes model to three glass box AI models: Decision Tree, Explainable Boosting Machine, and Automatic Piecewise Linear Regression. These models were used to generate four types of explainable …

Leveraging Graph Neural Networks For Explainable And Adaptive Seismic Risk Modelling (Work In Progress)), Shivam Tyagi, Musfira Jilani

Research Outputs: 2025-Present

Seismic risk modeling predicts the likelihood of earthquake occurrences in different regions over time. This presents a distinctive challenge as both the spatial structure of underlying geological features and the temporal evolution of seismic events must be accounted for accurately. In addition, for planners and decision-makers, it is essential that such models are not only accurate but also explainable. However, most existing models lack explainability, which limits their usefulness. This research proposes a novel graph neural network based seismic risk modeling methodology that is both explainable as well as adaptable, as it allows decision-makers the flexibility to choose the risk …