Physical Sciences and Mathematics Commons^™

Particle Acceleration At The Discontinuous Flow Boundary Of Collimated Cylindrical Jets, Stephen O'Sullivan, Andrew M. Taylor, Brian Reville

Conference Papers

We revisit the issue of particle acceleration at the interface between a collimated laminar jet and a static ambient medium. The contrast between standard diffusive scattering treatments and anomalous transport in synthetic field constructions is explored. A particular emphasis is placed on the necessity for physically consistent particle transport considerations. The temporal, spatial and spectral features of the process are discussed, in the context of potential UHECR production, as well as further observational consequences.

Lessons From The Classroom – Assessing The Work Of Postgraduate Students To Support Better Hygrothermal Risk Assessment, Joseph Little, Beñat Arregi, Christian Bludau

Conference papers

The widespread adoption of transient simulation modelling tools by building design professionals to support hygrothermal risk assessment of building design specifications is a crucial component in a multi-pronged drive to reduce moisture risk in buildings. Structured upskilling is essential. Much can be learnt about the ways practitioners use such tools by reviewing the work of professional postgraduate student groups. Such review could inform the creation of a user protocol. Peer-review under the responsibility of the organizing committee of the ICMB21.

Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney

Articles

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the …

Adapting An Agent-Based Model Of Infectious Disease Spread In An Irish County To Covid-19, Elizabeth Hunter, John D. Kelleher

Articles

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of …

Monitoring Quality Of Life Indicators At Home From Sparse And Low-Cost Sensor Data., Dympna O'Sullivan, Rilwan Basaru, Simone Stumpf, Neil Maiden

Conference papers

Supporting older people, many of whom live with chronic conditions or cognitive and physical impairments, to live independently at home is of increasing importance due to ageing demographics. To aid independent living at home, much effort is being directed at reliably detecting activities from sensor data to monitor people’s quality of life or to enhance self-management of their own health. Current efforts typically leverage smart homes which have large numbers of sensors installed to overcome challenges in the accurate detection of activities. In this work, we report on the results of machine learning models based on data collected with a …

Check Your Tech, Whose Responsibility Is It When Cyberharassment Occurs?, Dympna O'Sullivan, Damian Gordon, Michael Collins, Emma Murphy

Conference papers

Social media has become a dominant aspect of many people’s lives in many countries. Unfortunately that resulted in widespread issues of bullying and harassment. While frequently this harrassment is intentional, there have been occasions where automated processes have been inadvertently responsible for this sort of harassment. The software tools that allow people to harass others could have further features added to them to reduce the amount of harassment that occurs, but more often than not, where programmers are developing these systems then don’t anticipate the range of ways that these technologies will be used (this is called “consequence scanning”). The …

Is Twitter A Bad Place? The Responsibility That Social Media May Have Had In The 2021 Storming Of Capitol Hill., Ioannis Stavrakakis, Damian Gordon, Dympna O'Sullivan, Andrea Curley

Conference papers

The events of 6 th January 2021 in the United States of America, where rioters stormed the heart of their democracy, the US Capitol Complex (which houses their bicameral parliament) were shocking to see. The reasons for this riot were myriad, including to protest the outcomes of the presidential elections and two senate elections, as well as to prevent the counting that day of the electoral votes that formally certify the election result. These events will be analysed and reflected upon for years to come, and blame will be placed at many people’s doors, and inevitability one that has already …

Internet Of Medical Things (Iomt): Overview, Emerging Technologies, And Case Studies, Sahshanu Razdan, Sachin Sharma

Articles

No abstract provided.

Raman Spectroscopic Characterisation Of Non Stimulated And Stimulated Human Whole Saliva, Genecy Calado, Isha Behl, Hugh Byrne, Fiona Lyng

Articles

Human saliva is a unique biofluid which can reflect the physiopathological state of an individual. The wide spectrum of molecules present in saliva, compounded by the close association of salivary composition to serum metabolites, can provide valuable information for clinical diagnostic applications through highly sensitive vibrational spectroscopic techniques such as Raman spectroscopy. However, the nature of saliva, in terms of collection and patient-related characteristics, can be considered factors which may strongly affect the Raman spectral profile of salivary samples and disrupt the search for specific salivary biomarkers in the detection of diseases. The main objective of this study was to …

Pothole Detection Under Diverse Conditions Using Object Detection Models, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever

Conference papers

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalisable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles, and resolutions. In this paper, we present our …

The Effects Of Differences In Vaccination Rates Across Socioeconomic Groups On The Size Of Measles Outbreaks, Elizabeth Hunter, John D. Kelleher

Conference papers

Vaccination rates are often presented at the level of a country or region. However, within those areas there might be geographic or demographic pockets that have higher or lower vaccination rates. We use an agent-based model designed to simulate the spread of measles in Irish towns to examine if the effectiveness of vaccination rates to reduce disease at a population level is sensitive to the uniformity of vaccinations across socioeconomic groups. We find that when vaccinations are not applied evenly across socioeconomic groups we see more outbreaks and outbreaks with larger magnitudes.

On The Socles Of Fully Inert Subgroups Of Abelian P-Groups, Andrey R. Chekhlov, Peter V. Danchev, Brendan Goldsmith

Articles

We define the so-called fully inert socle-regular and weakly fully inert socle-regular Abelian p-groups and study them with respect to certain of their numerous interesting properties. For instance, we prove that in the case of groups of length ω, these two group classes coincide, but that in the case of groups of length ω+1, they differ. Some structural and characterization results are also obtained. The work generalizes concepts which have been of interest recently in the theory of entropy in algebra and builds on recent investigations by Danchev and Goldsmith (Arch Math (3) 92:191–199, 2009; J Algebra 323:3020–3028, 2010).

On The Socles Of Characteristically Inert Subgroups Of Abelian P-Groups, Andrey R. Chekhlov, Peter V. Danchev, Brendan Goldsmith

Articles

We define the notion of a characteristically inert socle-regular Abelian p-group and explore such groups by focussing on their socles, thereby relating them to previously studied notions of socle-regularity. We show that large classes of p-groups, including all divisible, totally projective and torsion-complete p-groups, share this property when the prime p is odd. The present work generalizes notions of full inertia intensively studied recently by several authors and is a development of a recent work of the authors published in Mediterranean J. Math. (2021).

An Analysis Of The Interpretability Of Neural Networks Trained On Magnetic Resonance Imaging For Stroke Outcome Prediction, Esra Zihni, John D. Kelleher, Bryony Mcgarry

Conference papers

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

Interrupting The Propaganda Supply Chain, Kyle Hamilton, Bojan Bozic, Luc Longo

Conference papers

In this early-stage research, a multidisciplinary approach is presented for the detection of propaganda in the media, and for modeling the spread of propaganda and disinformation using semantic web and graph theory. An ontology will be designed which has the theoretical underpinnings from multiple disciplines including the social sciences and epidemiology. An additional objective of this work is to automate triple extraction from unstructured text which surpasses the state-of-the-art performance.

Virtual Network Function Embedding Under Nodal Outage Using Deep Q-Learning, Swarna Bindu Chetty, Hamed Ahmadi, Sachin Sharma, Avishek Nag

Articles

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear …

Metrics For Performance Quantification Of Adaptive Mesh Refinement, Nicole Beisiegel, Cristóbal E. Castro, Jörn Behrens

Articles

Non-uniform, dynamically adaptive meshes are a useful tool for reducing computational complexities for geophysical simulations that exhibit strongly localised features such as is the case for tsunami, hurricane or typhoon prediction. Using the example of a shallow water solver, this study explores a set of metrics as a tool to distinguish the performance of numerical methods using adaptively refined versus uniform meshes independent of computational architecture or implementation. These metrics allow us to quantify how a numerical simulation benefits from the use of adaptive mesh refinement. The type of meshes we are focusing on are adaptive triangular meshes that are …

Wider Vision: Enriching Convolutional Neural Networks Via Alignment To External Knowledge Bases, Xuehao Liu, Sarah Jane Delany, Susan Mckeever

Conference papers

Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, the understanding of hidden feature map activations is limited by the discriminative knowledge gleaned during training. The aim of our work is to explain and expand CNNs models via the mirroring or alignment of the network to an external knowledge base. This will allow us to give a semantic context or label for each visual feature. Using the resultant aligned embedding space, we can match CNN feature activations to nodes …

Using A Hybrid Agent-Based And Equation Based Model To Test School Closure Policies During A Measles Outbreak, Elizabeth Hunter, John D. Kelleher

Articles

Background

In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest …

The Prospect Of Microwave Heating: Towards A Faster And Deeper Crack Healing In Asphalt Pavement, Shi Xu, Xueyan Liu, Amir Tabakovic, Erik Schlangen

Articles

Microwave heating has been shown to be an effective method of heating asphalt concrete and in turn healing the damage. As such, microwave heating holds great potential in rapid (1–3 min) and effective damage healing, resulting in improvement in the service life, safety, and sustainability of asphalt pavement. This study focused on the microwave healing effect on porous asphalt concrete. Steel wool fibres were incorporated into porous asphalt to improve the microwave heating efficiency, and the optimum microwave heating time was determined. Afterwards, the microwave healing efficiency was evaluated using a semi–circular bending and healing programme. The results show that …

Development Of 3d In Vitro Tissue Models For The Analysis Of Solar Radiation Damage Of Skin, Ulises Lopez Gonzalez

Doctoral

The aim of this work was to investigate changes to the molecular composition and conformation of HaCaT cells as a result of simulated solar radiation in a 3D in vitro skin model by Raman spectroscopy. The process to achieve this goal was performed in three main stages: (1) optimisation of the working concentration and volume of two 3D membranes, used as a structural support in the skin model; (2) the construction of the 3D in vitro skin model and; (3) the investigation of the dose-dependent effects of solar radiation on HaCaT cells in the skin model in comparison with the …

Diagnostics Of A Large Volume Pin-To-Plate Atmospheric Plasma Source For The Study Of Plasma Species Interactions With Cancer Cell Cultures, Laurence Scally, Chaitanya Sarangapani, Brijesh Tiwari, Renee Malone, Hugh Byrne, James Curtin, P.J. Cullen

Articles

A large gap pin-to-plate, atmospheric pressure plasma reactor is demonstrated as means of in vitro study of plasma species interactions with cell cultures. By employing optical emission and optical absorption spectroscopy, we report that the pin-to-pate plasma array had an optimal discharge frequency for cell death of 1000 Hz in ambient air for the target cancer cell line; human glioblastoma multiform (U-251MG). The detected plasma chemistry contained reactive oxygen and nitrogen species including OH, N₂, N₂⁺, and O₃. We show that, by varying the plasma discharge frequency, the plasma chemistry can be tailored …

Impact Of Dynamic Sub-Populations Within Grafted Chains On The Protein Binding And Colloidal Stability Of Pegylated Nanoparticles, Delyan Hristov, Hender Lopez, Yannick Ortin, Kate O'Sullivan, Kenneth A. Dawson, Dermot F. Brougham

Articles

Polyethylene glycol grafting has played a central role in preparing the surfaces of nano-probes for biological interaction, to extend blood circulation times and to modulate protein recognition and cellular uptake. However, the role of PEG graft dynamics and conformation in determining surface recognition processes is poorly understood primarily due to the absence of a microscopic picture of the surface presentation of the polymer. Here a detailed NMR analysis reveals three types of dynamic ethylene glycol units on PEG-grafted SiO2 nanoparticles (NPs) of the type commonly evaluated as long-circulating theranostic nano-probes; a narrow fraction with fast dynamics associated with the chain …

The Potential Of Raman Spectroscopy In The Diagnosis Of Dysplastic And Malignant Oral Lesions, Ola Ibrahim, M. Toner, Steven Flint, Hugh Byrne, Fiona Lyng

Articles

Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter and intra observer and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were …

Biomedical Applications Of Vibrational Spectroscopy: Oral Cancer Diagnostics, Hugh Byrne, Isha Behl, Genecy Calado, Ola Ibrahim, M. Toner, Sheila Galvin, Claire M. Healy, Steven Flint, Fiona Lyng

Articles

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

The 12th Annual Graduate Research Symposium 2021 Poster Tu Dublin: How To Recruit And Retain Women In Computer Science, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany

Other resources

While in recent decades a number of efforts have been coordinated to address the issue of gender imbalance in STEM (science, technology, engineering and mathematics) disciplines, the problem still persists. Many authors speak of the ‘leaky’ pipeline metaphor that describes the loss of women in STEM areas before reaching senior roles. Research shows that women who leave are unlikely to return. The issue is particularly severe in the area of computer science, where women represent less than 20% of the labour force across the EU.

This poster introduces a summary of findings from the literature on how to effectively recruit …

Existing Competencies In The Teaching Of Ethics In Computer Science Faculties, Ethics4eu Consortium

Reports

This report is one of the deliverables for the Ethics4EU project. It presents results obtained from a survey conducted in early 2020 that polled faculty from Computer Science and related disciplines on teaching practices in Computer Ethics in Computer Science across Europe. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not Computer Ethics is taught to Computer Science students at each institution, the reasons why Computer Ethics is or is not taught, how Computer Ethics is taught (for example, as a standalone course or embedded within other courses), the …

Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver

Dissertations

Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between …

A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran

Dissertations

This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design …

Improving A Network Intrusion Detection System’S Efficiency Using Model-Based Data Augmentation, Vinicius Waterkemper Lodetti

Dissertations

A network intrusion detection system (NIDS) is one important element to mitigate cybersecurity risks, the NIDS allow for detecting anomalies in a network which may be a cyberattack to a corporate network environment. A NIDS can be seen as a classification problem where the ultimate goal is to distinguish between malicious traffic among a majority of benign traffic. Researches on NIDS are often performed using outdated datasets that don’t represent the actual cyberspace. Datasets such as the CICIDS2018 address this gap by being generated from attacks and an infrastructure that reflects an up-to-date scenario.

A problem may arise when machine …