Physical Sciences and Mathematics Commons^™

Immobilized-Enzyme Reactors Integrated Into Analytical Platforms: Recent Advances And Challenges, Bert Wouters Dr, Sinead Currivan, Noor Abdulhussain, T. Hankemeier, Peter J. Schoenmakers

Articles

Immobilized-enzyme reactors (IMERs) are flow-through devices containing enzymes that are physically confined or localized with retention of their catalytic activities. IMERs can be used repeatedly and continuously and have been applied for (bio)polymer degradation, proteomics, biomarker discovery, inhibitor screening, and detection. Online integration of IMERs with analytical instrumentation, such as high-performance liquid chromatography (HPLC) systems, reduces the time needed for multi-step workflows, reduces the need for sample handling, and enables automation. However, online integration can also be challenging, as reaching its full potential requires complex instrumental setups and experienced users. This review aims to provide an assessment of recent advances …

Classification Of Explainable Artificial Intelligence Methods Through Their Output Formats, Giulia Vilone, Luca Longo

Articles

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. …

Adaptable And Reusable Educational ‘Bricks’ For Teaching Computer Science Ethics, Andrea Curley, Damian Gordon, Ioannis Stavrakakis, Anna Becevel, J.P. Gibson, Dympna O'Sullivan

Conference papers

No abstract provided.

Multi-Modal Self-Supervised Representation Learning For Earth Observation, Pallavi Jain, Bianca Schoen Phelan, Robert J. Ross

Conference papers

Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised …

A Health Elearning Ontology And Procedural Reasoning Approach For Developing Personalized Courses To Teach Patients About Their Medical Condition And Treatment, Martin Michalowski, Szymon Wilk, Wojtek Michalowski, Dympna O'Sullivan, Silvia Bonaccio, Enea Parimbelli, Marc Carrier, Grégoire Le Gal, Stephen Kingwell, Mor Peleg

Articles

We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific …

Biochemical Impact Of Solar Radiation Exposure On Human Keratinocytes Monitored By Raman Spectroscopy; Effects Of Cell Culture Environment, Ulises Lopez Gonzalez, Alan Casey, Hugh Byrne

Articles

Understanding and amelioration of the effects of solar radiation exposure are critical in preventing the occurrence of skin cancer. Towards this end, many studies have been conducted in 2D cell culture models under simplified and unrealistic conditions. 3D culture models better capture the complexity of in vivo physiology, although the effects of the 3D extracellular matrix have not been well studied. Monitoring the instantaneous and resultant cellular responses to exposure, and the influence of the 3D environment, could provide an enhanced understanding of the fundamental processes of photocarcinogenesis. This work presents an analysis of the biochemical impacts of simulated solar …

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.

The Design Of A Framework For The Detection Of Web-Based Dark Patterns, Andrea Curley, Dympna O'Sullivan, Damian Gordon, Brendan Tierney, Ioannis Stavrakakis

Conference Papers

In the theories of User Interfaces (UI) and User Experience (UX), the goal is generally to help understand the needs of users and how software can be best configured to optimize how the users can interact with it by removing any unnecessary barriers. However, some systems are designed to make people unwillingly agree to share more data than they intend to, or to spend more money than they plan to, using deception or other psychological nudges. User Interface experts have categorized a number of these tricks that are commonly used and have called them Dark Patterns. Dark Patterns are varied …

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 …

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 …

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 …

Temperature-Sensitive Holograms With Switchable Memory, Muhammad Irfan Dr, Suzanne Martin Dr, Izabela Naydenova Prof

Articles

Abstract

Herein, the dynamics and reversibility of temperature-induced changes in volume phase holographic gratings (VPHGs) recorded in the low-toxicity self-processing photopolymer poly(N-isopropyl acrylamide) (poly-NIPA) are reported on. Transmission and reflection gratings are exposed to temperatures ranging 8–60 °C to study the effect of temperature on their properties. First, the role of the photopolymer chemical composition is investigated by comparing layers containing NIPA with those containing acrylamide (AA) and measuring the normalized diffraction efficiency (DE) at 60 °C; higher thermal sensitivity is observed for poly-NIPA gratings (24% drop) versus low sensitivity for AA gratings (2% increase). The impact of …

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 …

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 …

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 …

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 …

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 …

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 …