Digital Commons Network^™

Development Of Methods To Quantify The Affective Response To Food Product Consumption Based On Kansei Engineering And Their Application In New Product Development, Dietmar Weiss

Dissertations

Consumer research, crucial to the success of new products, relies traditionally on rational judgements and opinions, which may not reflect the real reasons for consumer choice. The goal of this research was to apply and further develop the concepts of Kansei engineering to quantify an emotional response to product use. In addition to the application of the traditional Kansei engineering methods, three innovations were introduced. Firstly, the use of images instead of words to define the emotional space and further limit the interference of cognitive processes in the outcome. Secondly, the application of orthogonal arrays in product designs. Thirdly, the …

Triggering Students' Lifelong Learning Competencies Through A Personal Development Process – Case Studies From Belgium, Ireland & Finland, Lynn Van Den Broeck, Rani Dujardin, Sofie Craps, Johanna Naukkarinen, Una Beagon, Caitríona De Paor, Aimee Byrne

Conference Papers

No abstract provided.

Supporting Educators To Stimulate Engineering Students' Lifelong Learning Competencies & Personal Development Process, Lynn Van Den Broeck, Rani Dujardin, Sofie Craps, Una Beagon, Aimee Byrne, Caitriona Depaor, Johanna Naukkarinen

Articles

This paper explores the possibilities to develop students' lifelong learning (LLL) competencies via supporting their personal development process (PDP) in engineering education. Despite the recognized importance of LLL, its embedment in learning outcomes remains limited. A survey revealed that both engineering students and educators value LLL competencies, but perceive them to be taught and evaluated only to a limited extent. This paper introduces a PDP model to support educators in fostering students' LLL competencies. The model, developed through literature review and educators' input, includes (1) a matrix with various interventions, such as e-portfolios, reflective writing, and student-centred teaching, categorised by …

Irish Policy For Abnormal Load Movements, Lorcan Connolly, Alexandra Micu, Caitríona De Paor, Eugene Obrien, Fergal Cahill

Conference Papers

Currently in Ireland, any vehicle that falls outside the limits of the Road Traffic (Construction & Use of Vehicles) Regulations 2003 must obtain a permit for its movement. Depending on the vehicle configuration and the proposed route, hauliers may be required to apply for permits from the national police (An Garda Síochána) and one or more Local Authorities. There is no requirement for the haulier to liaise with Transport Infrastructure Ireland (the National Road Authority in Ireland); the Department of Transport; relevant maintaining organisations such as Motorway Maintenance Contractors and/or PPP Companies; or the Road Safety Authority. There is currently …

Materiality And The Machine: Maximising Material Experiences In The Age Of Ai, Ceri Almrott, Keith Colton, Mark Ennis, Ina O'Connor

Conference Papers

Artificial Intelligence (AI) and related technologies are expected to significantly impact the future of design, but traditional design methods are still important. Material Intelligence (MI) is crucial for product designers, and a structured four-year approach to undergraduate design education centred around experiential learning through MI is presented. The approach emphasises hands-on experimentation, integrates MI into the core curriculum, and prioritises interdisciplinary collaboration to produce innovative, functional, and sustainable design solutions.

Existing Gap Analysis Tool Inventory, Rahat Ullah, Adhban Omar Ahmed Farea, Joseph R. Harrington, Ted Mckenna, Sean Carroll

Tools

The Digital Performance Indicator (DPI) is a valuable tool for organisations aiming to evaluate their digital capabilities. It is specifically designed to offer insights into how an organisation's digital performance compares with that of its peers. This indicator functions as a strategic tool, providing a clear understanding of an organisation's position within the digital landscape and offering actionable recommendations for enhancing its digital capabilities. The DPI is a dynamic resource that enables organisations to make informed decisions and maintain competitiveness in an increasingly digitised business environment.

111 Crawling Before We Walk: Transdisciplinary Insights Into Improving The Liveability And Walkability Of Campus Settings, Lorraine D'Arcy, Eoin Mcgillicuddy, Caren Gallagher, Leo Mcconnell

Conference Papers

Since the 1960s, development patterns influenced by upstream land use planning policies have favoured a more suburban car-based approach for street and neighbourhood design (McGrath, 1992), subsequently influencing transport system needs and scheme design. In turn, transport policy and design influence population physical activity levels and emissions. As we moved to this more suburban design, our university campuses followed suit and now we have challenges that deter physical activity to and through our university campuses. Furthermore, air quality issues encouraged by a suburban design can enhance an individual's discomfort in participating in physical activity and may influence their decision to …

Antibiotics Misuse And Antimicrobial Resistance Development In Agriculture: A Global Challenge, Baljit Singh, Abhijnan Bhat, Kamna Ravi

Articles

No abstract provided.

Arsenic Contamination Needs Serious Attention: An Opinion And Global Scenario, Abhijnan Bhat, Kamna Ravi, Furong Tian, Singh Baljit

Articles

Arsenic (As) contamination is a serious global concern, polluting our natural resources, including water and soil, and posing a danger to the environment and public health. Arsenic is present in the groundwater of several countries and this contaminated water is used for irrigation, drinking, and food preparation, which poses the greatest threat to public health. Nearly 106 countries are affected by groundwater arsenic contamination and an estimated 230 million individuals worldwide are exposed to its adverse health effects, including increased cancer risks, associated cardiovascular disease and diabetes, skin lesions, neurological effects, kidney damage, and foetal or cognitive-development-related complications. Arsenic is …

Eskape: Navigating The Global Battlefield For Antimicrobial Resistance And Defense In Hospitals, Kamna Ravi, Baljit Singh

Articles

The current healthcare environment is at risk due to the facilitated transmission and empowerment of the ESKAPE pathogens, comprising of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species. These pathogens have posed significant challenges to global public health and the threat has only amplified over time. These multidrug-resistant bacteria have become adept at escaping the effects of conventional antibiotics utilized, leading to severe healthcare-associated infections and compromising immunocompromised patient outcomes to a greater extent. The impact of ESKAPE pathogens is evident in the rapidly rising rates of treatment failures, increased mortality, and elevated healthcare costs. …

Multi-Stakeholder Perspective On Human-Ai Collaboration In Industry 5.0, Thomas Hoch, Jorge Martinez-Gil, Mario Pichler, Agastya Silvina, Bernhard Heinzl, Bernhard Moser, Dimitris Eleftheriou, Hector Diego Estrada-Lugo, Maria Chiara Leva

Books/Book Chapters/ Proceedings

AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.

Enhancing Control Room Operator Decision Making, Joseph Mietkiewicz, Ammar N. Abbas, Chidera W. Amazu, Gabriele Baldissone, Anders L. Madsen, Micaela Demichela, Maria Chiara Leva

Articles

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. …

Exploring The Influence Of Human System Interfaces: Introducing Support Tools And An Experimental Study, Chidera W. Amazu, Joseph Mietkiewicz, Ammar N. Abbas, Houda Briwa, Andres Alonso-Perez, Gabriele Baldissone, Davide Fissore, Micaela Demichela, Maria Chiara Leva

Articles

Situational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators' cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing …

Mental Workload Classification And Tasks Detection In Multitasking: Deep Learning Insights From Eeg Study, Miloš Pušica, Aneta Kartali, Luka Bojović, Ivan Gligorijević, Jelena Jovanović, Maria Chiara Leva, Bogdan Mijović

Articles

While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual's effort, mental capacity, or cognitive resources utilized while performing a task. MWL in multitasking scenarios is often closely linked with the quantity of tasks a person is handling within a given timeframe. In this study, we challenge this hypothesis from the perspective of electroencephalography (EEG) using a deep learning approach. We conducted an EEG experiment with 50 participants performing NASA Multi-Attribute Task Battery II (MATB-II) under 4 different task load …

Experiment Data: Human-In-The-Loop Decision Support In Process Control Rooms, Chidera Winifred Amazu, Joseph Mietkiewicz, Ammar N. Abbas, Houda Briwa, Andres Alonso-Perez, Gabriele Baldissone, Micaela Demichela, Davide Fissore, Anders L. Madsen, Maria Chiara Leva

Articles

These datasets contain measures from multi-modal data sources. They include objective and subjective measures commonly used to determine cognitive states of workload, situational awareness, stress, and fatigue using data collection tools such as NASA-TLX, SART, eye tracking, EEG, Health Monitoring Watch, a survey to assess training, and a think-aloud situational awareness assessment following the SPAM methodology. Also, data from a simulation formaldehyde production plant based on the interaction of the participants in a controlled control room experimental setting is included. The interaction with the plant is based on a human-in-the-loop alarm handling and process control task flow, which includes Monitoring, …

Collaborative Intelligence For Safety-Critical Industries: A Literature Review, Inês F. Ramos, Gabriele Gianini, Maria Chiara Leva, Ernesto Damiani

Articles

While AI-driven automation can increase the performance and safety of systems, humans should not be replaced in safety-critical systems but should be integrated to collaborate and mitigate each other's limitations. The current trend in Industry 5.0 is towards human-centric collaborative paradigms, with an emphasis on collaborative intelligence (CI) or Hybrid Intelligent Systems. In this survey, we search and review recent work that employs AI methods for collaborative intelligence applications, specifically those that focus on safety and safety-critical industries. We aim to contribute to the research landscape and industry by compiling and analyzing a range of scenarios where AI can be …

A Review On Machine Learning Methods For Customer Churn Prediction And Recommendations For Business Practitioners, Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo

Articles

Due to market deregulation and globalisation, competitive environments in various sectors continuously evolve, leading to increased customer churn. Effectively anticipating and mitigating customer churn is vital for businesses to retain their customer base and sustain business growth. This research scrutinizes 212 published articles from 2015 to 2023, delving into customer churn prediction using machine learning methods. Distinctive in its scope, this work covers key stages of churn prediction models comprehensively, contrary to published reviews, which focus on some aspects of churn prediction, such as model development, feature engineering and model evaluation using traditional machine learning-based evaluation metrics. The review emphasises …

Speculative Enquiries: The Third Annual Conference Of The European Culture And Technology Laboratory., Connell Vaughan, Marinos Koutsomichalis, Noel Fitzpatrick

Conference Proceedings

The third annual conference of the ECT Lab+ was held over three days in November 2023 in Limassol at the Media Arts & Design Research Lab (MADLab), Cyprus University of Technology. The conference brought together experts from the Arts, Humanities, Social Sciences, Technology, and other fields to explore ways of enquiry that enrich (non-)positivist ways of knowing as well as post-structuralist critique towards them. Given the political, representative and epistemic crises of the current times, there is a need for transdisciplinary responses to overcome siloed thinking of academic fields of study and offer solutions by revisiting questions of speculation as …

An Explainable Approach To Understanding Gender Stereotype Text, Manuela Nayantara Jeyaraj, Sarah Jane Delany

Conference Papers

Gender Stereotypes refer to the widely held beliefs and assumptions about the typical traits, behaviours, and roles associated with a collective group of individuals of a particular gender in society. These typical beliefs about how people of a particular gender are described in text can cause harmful effects to individuals leading to unfair treatment. In this research, the aim is to identify the words and language constructs that can influence a text to be considered a gender stereotype. To do so, a transformer model with attention is fine-tuned for gender stereotype detection. Thereafter, words/language constructs used for the model's decision …

A Combined Experimental And Numerical Approach For Heat Transfer Enhancement In A Minichannel Using Half Rectified Pulsating Flows, P. S. Kumavat, S. Alimohammadi, S. M. O'Shaughnessy

Conference Papers

The miniaturization of electronic packages and their associated high-power density circuits requires more innovative cooling solutions. Single phase pulsating flows offer a promising solution due to their disruption of the thermal boundary layer. This study aims to bridge the knowledge gap by using experimental and computational methods to investigate the complex flow characteristics of laminar pulsating flows in a heated rectangular minichannel and couple that analysis with an investigation of thermal-hydraulic performance. Experimental analysis involves a uniformly heated thin foil approximating a constant heat flux bottom wall. Wall temperature measurements are recorded using an infrared thermography system. Analogous to the …

Modelling The Behaviour Of Rammed Earth Walls Using Finite Element Analysis, Eoghan Healy, Juan Pablo Osorio

Conference papers

Up to one in three people on Earth live in earthen buildings, with rammed earth being the most common form of earthen construction. Rammed earth is one of the oldest construction materials in the world. Rammed earth is coming into fashion in first-world countries due to its pleasant aesthetic and sustainable construction. In contrast, it can be a necessary form of construction in the global south as there may be no other choice. However, these homes are often constructed without technical advice or expertise, leading to dangerous situations for the inhabitants of these structures.

Augmenting Building Information Modeling (Bim) With Occupancy Data Using Internet Of Things (Iot) Devices., Aitor Arribas Velasco, Damon Berry, John Mcgrory

Conference papers

As people spend a lot of their time indoors and with the growth of IoT devices, there is evidence of increased use of smart devices to improve the indoor experience. A location-specific IoT hack is where the IoT hack is applied in an ad hoc fashion to a precise place or location. But the transportability of successful hacks from the original property to another site is not always straightforward, particularly when some equipment has been partially configured with device names and network addresses that differ from the original property configuration. A large portion of the challenge also relates to the …

Assessment Of Heavy Metals (Cr, Cu, Pb, And Zn) Bioaccumulation And Translocation By Erigeron Canadensis L. In Polluted Soil, Volodymyr Laptiev, Michelle Giltrap, Furong Tian, Nataliia Ryzhenko

Research Outputs: 2025-Present

This work aims to assess the bioavailability and bioaccumulation of Cr, Cu, Pb, and Zn in the soil–plant system (Erigeron canadensis L.) in the zone of anthropogenic impact in Dnipro city, a significant industrial and economic centre of Ukraine. Sampling was carried out at three locations at distances of 1.0 km, 5.5 km, and 12.02 km from the main emission sources associated with battery production and processing plants in Dnipro. The concentrations of heavy metals such as Cr, Cu, Pb, and Zn were analysed using atomic emission spectrometry from soil and parts of Erigeron canadensis L. The highest concentrations of …

Managing Human-Ai Collaborations Within Industry 5.0 Scenarios Via Knowledge Graphs: Key Challenges And Lessons Learned, Franz Krause, Heiko Paulheim, Elmar Kiesling, Kabul Kurniawan, Maria Chiara Leva, Hector Diego Estrada-Lugo, Gernot Stübl, Nazim Kemal Üre, Javier Dominguez-Ledo, Maqbool Khan, Pedro Demolder, Hans Gaux, Bernhard Heinzl, Thomas Hoch, Jorge Martinez-Gil, Agastya Silvina, Bernhard A. Moser

Research Outputs: 2025-Present

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping …

Circular Material Usage Strategies And Principles In Buildings: A Review, Paulo Santos, Génesis Camila Cervantes, Alicia Zaragoza-Benzal, Aimee Byrne, Ferhat Karaca, Daniel Ferrández, Adriana Salles, Luís Bragança

Articles

Building construction accounts for a significant proportion of global greenhouse gas emissions, raw material extraction, and waste production. Applying circular economy (CE) principles in the building construction industry would considerably reduce these values. However, uptake by the industry is relatively slow, which is largely attributed to sectoral barriers, including limitations in knowledge and experience. This review paper aims to assess and contribute to diminishing these obstacles by offering a comprehensive review of circular material usage principles and strategies within the construction sector. Opportunities and facilitators of change are also presented, including innovations and emerging technologies in recycling, digitization, robotic systems, …

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Outdoor Navigation Support Using Machine Learning For People With Visual Impairment, Fatmaelzahraa Eltaher

Doctoral

Navigating urban environments is challenging for People with Visual Impairments (PVI). Many PVI opt to use navigation systems, such as Google Maps. However, current navigation systems often lack information crucial for PVI, such as the presence of traffic lights or roadworks. Knowing these features would enable PVI to understand their surroundings better and choose routes that suit their preferences. For instance, 79.6\% of PVI surveyed in this thesis prefer road junctions (termed intersections) controlled by traffic lights.

This research contributes to advancing navigation support for PVI in urban environments by focusing on annotating maps with useful environmental information. It proposes …

Towards Interpretable Propaganda Detection In News Text Through The Use Of Rhetorical Devices As Features, Kyle Hamilton

Doctoral

This doctoral thesis presents research undertaken towards interpretable propaganda detection in news text. Propaganda and mis/disinformation in the media have been identified in the social sciences literature as a major threat to the functioning of democratic society. While mis/disinformation is false by definition, propaganda is intended to modify the beliefs and behaviors of the information consumer but does not have to be false. Propagandistic text is often characterized by the use of rhetorical devices and linguistic style designed to exploit the reader’s cognitive and emotional biases. One way of counteracting this effect is by shifting the focus from “what” is …

Multiple Sclerosis Diagnosis With Deep Learning And Explainable Ai, Nighat Bibi, Jane Courtney, Kathleen M. Curran

Conference papers

Diagnosing multiple sclerosis (MS) presents significant challenges due to its complex clinical presentation and the subjective interpretation of imaging findings. Machine learning (ML) and deep learning (DL) models, despite their potential, often exacerbate these challenges with their opaque decision-making processes, hindering clinical integration. This study addresses these limitations by employing eXplainable Artificial Intelligence (XAI) techniques, specifically integrating Grad-CAM within a Convolutional Neural Network (CNN) framework, EfficientNetB1, for the diagnosis of MS. The primary objective is to enhance the transparency and reliability of MS diagnosis by providing clear visual insights into the model’s decision-making process, while also identifying and mitigating potential …

Enhancing Multiple Sclerosis Diagnosis With Explainable Ai, Nighat Bibi, Jane Courtney, Kathleen M. Curran

Conference papers

Multiple sclerosis (MS) is a complex neurological disorder that requires precise diagnosis for effective treatment. This study aims to enhance MS diagnosis by integrating eXplainable Artificial Intelligence (XAI) techniques into a convolutional neural network (CNN) framework. The proposed model achieves high accuracy and provides visual explanations of its predictions. Using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, it highlights the most important regions in MRI images influencing the model’s decisions, adding transparency and trust to the diagnostic process. The CNN, trained on a dataset of FLAIR MRI images, demonstrates superior performance compared to existing models, with a final accuracy of …