Physical Sciences and Mathematics Commons^™

Ipq Bibliography Pac Km Qrm 2010 - 2023

Level 3

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Lipid Membrane Self-Assembly And Synthesis In The Lab By Rapid, Low-Cost Microfluidics Approaches, Kathleen Hardwick, Shane O'Reilly

SURE Journal: Science Undergraduate Research Experience Journal

Lipid membranes, which self-assemble in nature and under laboratory conditions, can form vesicles called liposomes. The main uses for liposomes are in drug delivery systems and as animal cell models. From the initial research into lipid membrane science, to the systematic literature review using Web of Science, to refining the project scope, to the description of the method, this project follows the process of developing a method for lipid membrane self-assembly and synthesis in the lab. The result was a rapid prototyping method for fabricating microfluidic devices to test different designs for channels, for use with a liposome synthesis protocol. …

Regulation Of Mirnas In Obesity And Diabetes, Eva Maher, Ava O'Meara, Cathy Brougham

SURE Journal: Science Undergraduate Research Experience Journal

Introduction: The prevalence of obesity and obesity-related secondary disorders has increased dramatically in recent years. Dysregulated microRNA (miRNA) expression has been linked to the development of obesity and its associated disorders. Adipose derived stem cells (ADSCs) are promising cell models of obesity, obesity-related inflammation and diabetes. Furthermore, miRNA expression profiles have emerged as promising targets for the novel treatment of diseases such as obesity. In order to enhance miRNA-based therapies, the miRNA expression profile of obesity and its related disorders must be monitored to further understand the underlying mechanisms of obesity and to identify novel targets.

Aim: The aim of …

The Potential Of Stem Cell Therapy In Multiple Sclerosis Treatment: A Review, Sorina Amarculesei, Ava O'Meara Cushen, Cathy Brougham

SURE Journal: Science Undergraduate Research Experience Journal

Multiple Sclerosis is an autoimmune and neurodegenerative disease of the central nervous system. There are currently 2.8 million people living with Multiple Sclerosis worldwide, including 9000 people in Ireland, with a prevalence of 193 per 100,000 people. Symptoms of Multiple Sclerosis include sensory loss, fatigue, cognitive dysfunction, spasticity, depression, optic neuritis, gait ataxia, diplopia, and loss of bladder control. Currently, there is no standardised treatment or cure for Multiple Sclerosis with many strategies focusing on symptoms. Stem cells have emerged as promising approaches for Multiple Sclerosis therapeutics. Clinical trials primarily focus on mesenchymal stem cell-based therapies for Multiple Sclerosis. Furthermore, …

Editorial - Sure Journal Vol 5, Anne M. Friel

SURE Journal: Science Undergraduate Research Experience Journal

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Development Of An Optical Test Bed For The Fabrication And Characterisation Of An Analog Holographic Wavefront Sensor, Emma Branigan, Andreas Zepp, Suzanne Martin, Matthew Sheehan, Szymon Gladysz, Kevin Murphy

Conference Papers

A new holographic recording setup has been developed for the fabrication of single- and multi-mode photopolymer-based analog holographic wavefront sensors. A second setup has been built and used to characterise the sensor at several wavelengths.

Exploring The Impact Of Signal Quality Enhancement On Heart Sound Classification Models, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Articles

Limited cardiology resources increase the urgency for automated heart disease screening for the general public. Heart sound diagnostic models have been recently employed as a cost-effective solution for the initial screening of heart disease. Noise in heart sound recordings, however, can reduce the performance of such data-driven models. Various quality enhancement approaches have been adopted to alleviate the destructive impact of noise on model performance. One approach is universal noise reduction which applies denoising techniques to recordings, irrespective of their noise level. The second approach is targeted noise reduction, which applies denoising solely to recordings deemed to need it, based …

Renormalized Stress-Energy Tensor For Scalar Fields In Hartle-Hawking, Boulware, And Unruh States In The Reissner-Nordström Spacetime, Julio Arrechea, Cormac Breen, Adrian Ottewill, Peter Taylor

Articles

In this paper, we consider a quantum scalar field propagating on the Reissner-Nordström black hole spacetime. We compute the renormalized stress-energy tensor for the field in the Hartle-Hawking, Boulware and Unruh states. When the field is in the Hartle-Hawking state, we renormalize using the recently developed “extended coordinate” prescription. This method, which relies on Euclidean techniques, is very fast and accurate. Once, we have renormalized in the Hartle-Hawking state, we compute the stress-energy tensor in the Boulware and Unruh states by leveraging the fact that the difference between stress-energy tensors in different quantum states is already finite. We consider a …

Raman Spectroscopic Analysis Of Human Serum Samples Of Convalescing Covid-19 Positive Patients, Hugh Byrne, Naomi Jackson, Jaythoon Hassan

Articles

Rapid screening, detection and monitoring of viral infection is of critical importance, as exemplified by the rapid spread of SARS-CoV-2, leading to the worldwide pandemic of COVID-19. This is equally the case for the stages of patient convalescence as for the initial stages of infection, to understand the medium and long terms effects, as well as the efficacy of therapeutic interventions. Optical spectroscopic techniques potentially offer an alternative to currently employed techniques of screening for the presence, or the response to infection. In this study, the ability of Raman spectroscopy to distinguish between samples of the serum of convalescent COVID-19 …

Optimization Of Biomedical Imaging Filters For Use In Recaptured Identity Document Classification, John Magee, Stephen Sheridan Phd, Christina Thorpe Phd

Conference papers

As banks and online financial institutions move toward full remote onboarding services, the attack vectors for bad actors increases to include those of recaptured identity documents. This type of fraud opens banking customers to potential crimes of identity theft, as well as causing reputational damage to the institutions involved. In this paper we extend existing research focusing on the use of biomedical imaging filters and their usefulness when classifying recaptured identity documents. We perform a grid search and demonstrate that different filter configurations exist that dramatically reduce the classification error rates compared to those achieved using only the default filter …

Exploring Wav2vec 2.0 Model For Heart Murmur Detection, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Conference papers

The lack of access to cardiology resources in many regions of the world has motivated the development of automatic diagnostic systems based on cardiac signals. In recent years, a wide range of supervised learning models have been proposed that can make an initial diagnosis of heart disease from heart sounds. To achieve high accuracy, however, such supervised learning models generally require a large amount of labeled data, which can be costly to obtain. In this regard, self-supervised learning has been recently employed to reduce the over-reliance on annotated data. Wav2vec 2.0 is an audio self-supervised learning model that has shown …

Sigite'23 Poster And Extended Abstract: The Proof Of Gold Is Fire: Measuring Stress To Show Impact Of Gender Based Initiatives In Computing Education, Alina Berry, Sarah Jane Delany

Other resources

Many initiatives across the world try to address the issue of gender imbalance in computing education. Evaluation of the impact of these initiatives is not always straightforward. Some interventions are able to demonstrate impact based on recruitment and retention numbers of underrepresented gender groups, in particular, women, often requiring a longer-term study to see impact. Others, especially shorter term interventions and those taking place during the teaching and learning process, frequently use feedback and other instruments as a measure of impact. This work reviews existing evaluation methods from the literature, categorises them as using statistical data, feedback or instruments, grouping …

Raman Spectral Cytopathology For Cancer Diagnostic Applications, Fiona Lyng, Damian Traynor, Isha Behl, Declan O'Dea, Hugh Byrne

Books/Book Chapters

Histopathology is currently regarded as the “gold standard” patient sampling technique for cancer diagnostics. It involves extraction of a tissue sample, or biopsy, which is then sectioned and stained for evaluation by a pathologist [1]. Cytopathology, on the other hand, is the field of disease diagnosis at the cellular level [2], necessitating only the microscopic evaluation of samples of cells which can be harvested by less invasive methods such as exfoliative brushing, needle probes or from biofluids. Based on the identification of abnormalities in cellular appearance, cytopathology is currently widely used to aid in the screening of cancer, as well …

A Deep Learning-Based Object Detection Framework For Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images, Ibrahim Hassan Syed, Susan Mckeever Dr., Kieran Feighan, David Power, Dympna O'Sullivan

Conference papers

Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for …

Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework And Application For Decision Support For Operators In Control Rooms, Joseph Mietkiewicz, Ammar N. Abbas, Chidera W. Amazu, Anders L. Madsen, Gabriele Baldissone

Articles

In today’s complex industrial environment, operators are often faced with challenging situations that require quick and accurate decision-making. The human-machine interface (HMI) can display too much information, leading to information overload and potentially compromising the operator’s ability to respond effectively. To address this challenge, decision support models are needed to assist operators in identifying and responding to potential safety incidents. In this paper, we present an experiment to evaluate the effectiveness of a recommendation system in addressing the challenge of information overload. The case study focuses on a formaldehyde production simulator and examines the performance of an improved Human-Machine Interface …

An Investigation Into The Application Of The Meijering Filter For Document Recapture Detection, John Magee, Stephen Sheridan Phd, Christina Thorpe Phd

Conference papers

The proliferation of mobile devices allows financial institutions to offer remote customer services, such as remote account opening. Manipulation of identity documents using image processing software is a low-cost, high-risk threat to modern financial systems, opening these institutions to fraud through crimes related to identity theft. In this paper we describe our exploratory research into the application of biomedical image algorithms to the domain of document recapture detection. We perform a statistical analysis to compare different types of recaptured documents and train a support vector machine classifier on the raw histogram data generated using the Meijering filter. The results show …

A Method For Generating A Non-Manual Feature Model For Sign Language Processing, Robert G. Smith Dr, Markus Hofmann Dr

Articles

While recent approaches to sign language processing have shifted to the domain of Machine Learning (ML), the treatment of Non-Manual Features (NMFs) remains an open question. The principal challenge facing this method is the comparatively small sign language corpora available for training machine learning models. This study produces a statistical model which may be used in future ML, rules-based, and hybrid-learning approaches for sign language processing tasks. In doing so, this research explores the emerging patterns of non-manual articulation concerning grammatical classes in Irish Sign Language (ISL). The experimental method applied here is a novel implementation of an association rules …

Interpretable Timbre Synthesis Using Variational Autoencoders Regularized On Timbre Descriptors, Anastasia Natsiou, Luca Longo, Sean O'Leary

Conference papers

Controllable timbre synthesis has been a subject of research for several decades, and deep neural networks have been the most successful in this area. Deep generative models such as Variational Autoencoders (VAEs) have the ability to generate a high-level representation of audio while providing a structured latent space. Despite their advantages, the interpretability of these latent spaces in terms of human perception is often limited. To address this limitation and enhance the control over timbre generation, we propose a regularized VAE-based latent space that incorporates timbre descriptors. Moreover, we suggest a more concise representation of sound by utilizing its harmonic …

Exploring Mental Models For Explainable Artificial Intelligence: Engaging Cross-Disciplinary Teams Using A Design Thinking Approach, Helen Sheridan, Emma Murphy Dr., Dympna O'Sullivan Dr.

Conference papers

Exploring end-users’ understanding of Artificial Intelligence (AI) systems’ behaviours and outputs is crucial in developing accessible Explainable Artificial Intelligence (XAI) solutions. Investigating mental models of AI systems is core in understanding and explaining the often opaque, complex, and unpredictable nature of AI. Researchers engage surveys, interviews, and observations for software systems, yielding useful evaluations. However, an evaluation gulf still exists, primarily around comprehending end-users' understanding of AI systems It has been argued that by exploring theories related to human decision-making examining the fields of psychology, philosophy, and human computer interaction (HCI) in a more people-centric rather than product or technology-centric …

Optical Characterisation Of Holographic Diffusers And Bangerter Foils For Treatment Of Amblyopia, Matthew Hellis, Suzanne Martin, Matthew Sheehan, Kevin Murphy

Articles

Amblyopia is a significant issue for children worldwide, and current treatment methods have drawbacks that can hinder treatment effectiveness and/or patient experience. This study proposes a new treatment method using holographic diffusers while also comparing their optical characteristics to a current treatment method (Bangerter foils). Holographic diffusers were developed by optically patterning thin polymer layers on a micron scale. Two compositions of photopolymer (acrylamide and diacetone acrylamide based) are analysed herein. Characterisation shows that holographic diffusers of either composition can achieve a wide range of on-axis intensity reductions, allowing for precise and customisable treatment levels by altering recording exposure time …

Poly-Gan: Regularizing Polygons With Generative Adversarial Networks, Lasith Niroshan, James Carswell

Conference Papers

Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features …

Exploiting Association Rules Mining To Inform The Use Of Non-Manual Features In Sign Language Processing, Robert G. Smith

Other Resources

In recent years, the use of virtual assistants and voice user interfaces has become a latent part of modern living. Unseen to the user are the various artificial intelligence and natural language processing technologies, the vast datasets, and the linguistic insights that underpin such tools. The technologies supporting them have chiefly targeted widely used spoken languages, leaving sign language users at a disadvantage. One important reason why sign languages are unsupported by such tools is a requirement of the underpinning technologies for a comprehensive description of the language. Sign language processing technologies endeavour to bridge this technology inequality.

Recent approaches …

Modelling Hoe Performance With An Extended Source; Experimental Investigation Using Misaligned Point Sources, Jorge Lasarte, Kevin Murphy, Izabela Naydenova, Jesús Atencia, Mª Victoria Collados, Suzanne Martin

Conference Papers

Holographic Optical Elements (HOEs) have the potential to enable more compact, versatile and lightweight optical designs, but many challenges remain. Volume HOE’s have the advantage of high diffraction efficiency but they present both chromatic selectivity and chromatic dispersion which impact on their use with wide spectrum light sources. Single-colour LED sources have a narrow spectrum that reduces these issues and this makes them better suited for use with volume HOEs. However, the LED source size must be taken into consideration for compact volume HOE-LED systems. To investigate the design limits for compact HOE-LED systems, a theoretical and experimental study was …

An Exploration Of The Latent Space Of A Convolutional Variational Autoencoder For The Generation Of Musical Instrument Tones, Anastasia Natsiou, Sean O'Leary, Luca Longo

Conference papers

Variational Autoencoders (VAEs) constitute one of the most significant deep generative models for the creation of synthetic samples. In the field of audio synthesis, VAEs have been widely used for the generation of natural and expressive sounds, such as music or speech. However, VAEs are often considered black boxes and the attributes that contribute to the synthesis of a sound are yet unsolved. Existing research focused on the way input data can influence the generation of latent space, and how this latent space can create synthetic data, is still insufficient. In this manuscript, we investigate the interpretability of the latent …

Co-Designing Assistive Technology With And For Persons Living With Dementia, Dympna O'Sullivan, Jonathan Turner, Siobhan O'Neill, Micheal Wilson, Julie Doyle

Conference papers

Dementia is a chronic and progressive neurodegenerative illness, which can lead to significant difficulties in a person’s capacity to perform activities of daily living (ADLs) and engage in meaningful activities. There is an acute need, which digital health technologies can potentially fulfil, to provide proactive support for persons living with dementia (PLwD) and their caregivers. However, there is limited involvement of PLwD in the design of technology that could be used to support their personal plans for independent living at home. In this paper, we describe how we are employing a co-design methodology to support engagement in an assistive technology …

Analog Holographic Wavefront Sensor For Defocus And Spherical Aberration Measurement Recorded In A Photopolymer, Emma Branigan, Suzanne Martin, Matthew Sheehan, Kevin Murphy

Articles

An analog holographic wavefront sensor (AHWFS), for measurement of low and high order (defocus and spherical aberration) aberration modes has been developed as volume phase holograms in a photopolymer recording medium. This is the first time that high order aberrations such as spherical aberration can be sensed using a volume hologram in a photosensitive medium. Both defocus and spherical aberration were recorded in a multi-mode version of this AHWFS. Refractive elements were used to generate a maximum and minimum phase delay of each aberration which were multiplexed as a set of volume phase holograms in an acrylamide based-photopolymer layer. The …

Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert J. Ross, Susan Mckeever

Conference papers

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the …

Applying Positional Encoding To Enhance Vision-Language Transformers, Xuehao Liu, Sarah Jane Delany, Susan Mckeever

Conference papers

Positional encoding is used in both natural language and computer vision transformers. It provides information on sequence order and relative position of input tokens (such as of words in a sentence) for higher performance. Unlike the pure language and vision transformers, vision-language transformers do not currently exploit positional encoding schemes to enrich input information. We show that capturing location information of visual features can help vision-language transformers improve their performance. We take Oscar, one of the state-of-the-art (SOTA) vision-language transformers as an example transformer for implanting positional encoding. We use image captioning as a downstream task to test performance. We …

Fair4pghd: A Framework For Fair Implementation Over Pghd, Abdullahi Abubakar Kawu, Dympna O'Sullivan, Lucy Hederman, Mirjam Van Reisen

Articles

Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little is known about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a 5-stage framework that can be used to achieve this.

Determining The Proportionality Of Ischemic Stroke Risk Factors To Age, Elizabeth Hunter, John D. Kelleher

Articles

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over-or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, eg, risk factors contribute differently to stroke risk based on an individual’s age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data …