Physical Sciences and Mathematics Commons^™

Life Under Pressure: How Doubling The Genome Affects The Memory Of Environmental Stress In Worms, Aisling Phelan, Emma Bazzani, Clément Verdier, Laetitia Chauve, Aoife Mclysaght

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Pause For Policy - The Cognitive Cost Of Cannabis Use In Adolescence, Michael O'Connor, Linda Kelly, Emma O'Hora, Claire O'Doherty, Ciaran Brown, An Hsu, Sahar Riaz, Frank Crosson, Darren Roddy, Mary Cannon

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

The Future Of Metabolic Health: A Needle-Free Alternative For Blood Sugar Monitoring, Conor Cleary, Martin Bradley, Christopher Crosson, John Wade

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

From Recognition To Action: Advancing Nutrition Care In Irish Healthcare, Aoife Gillane, Sarah Donovan, Lisa Ryan

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Transforming Plastic For A Sustainable Planet: Next-Generation Materials That Biodegrade Naturally, Kristof Racz, Clement Higginbotham

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

The Dock Beetle: Reducing Costs And Pesticides In Irish Agriculture, Bianca Araujo, Daniel P. Fitzpatrick

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Implementation Of The Nature Restoration Law In The Eu - Pros, Cons And Impossibilities, Emma Mcdonagh, Liam Sunner

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Ireland’S Energy Transition: Unlocking The Potential Of Offshore Renewables, Caoimhe O'Hare, Madjid Karimirad, Gautam Baruah

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

From Conflict To Connection: Educators Leading The Shift Towards Restorative Practices (Rp) In Deis Primary Schools, Ellen Slattery, Clara Hoyne

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Bridging The Gap From Preschool To Primary: From Policy To Practice, Aimee O'Connor, Cóilín O’ Braonáin

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Editorial, Anne M. Friel, Brigid Hooban, Therese Montgomery, Anne Marie O'Brien, Cormac Quigley, Edel Mcneela, Eva Campion, James Walshe, Sinead Loughran

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Image Captioning Through The Lens Of The Gricean Maxims: Generating Meaningful And Relevant Image Descriptions, Annika Lindh

Doctoral

Image captioning models enable us to automatically generate natural language image descriptions for previously unseen images. It combines the two fields of computer vision and natural language generation, allowing models to interpret the con tent of an image and communicate that knowledge through natural language text.

Research into image captioning has the potential benefit of reducing the gap in digital information availability between fully sighted individuals and those who are visually impaired. However, automatically generated captions often fail to provide the required level of detail and specificity to achieve this goal. Furthermore, current standard evaluation methods are insufficient at measuring …

The Complexity Of Long-Distance Dependencies And Their Impact On Language Models, Abhijit Shrikant Mahalunkar

Doctoral

Sequential data modeling is an important challenge in various fields and in particular in natural language processing. Building effective sequential models faces a notable challenge in the form of Long-Distance Dependencies (LDDs) within the sequence data. Hence, successfully overcoming this challenge is imperative for developing robust and accurate sequential models across various domains and applications. To tackle this challenge, the first step is to conduct a detailed analysis of the complexity of LDDs observed in various sequence datasets. This thesis offers a thorough exploration and documentation of this analysis. An important finding from this thesis is the consistent patterns of …

Dataset Of Raman Spectra And Matlab Code For: Elucidating Time-Resolved Intracellular Metabolic Dynamics Via Label-Free Raman Microspectroscopy And 2d Correlation Spectroscopy, Zohreh Mirveis, Nitin Patil, Hugh J. Byrne

Other Resources

This dataset contains raw spectral data obtained from single-cell Raman microspectroscopy under two nutritional conditions: glucose alone and glucose supplemented with glutamine. LLC-MK2 cells were starved for 2 h, then exposed to nutrients and fixed every 15 min for up to 120 min. At each time point, spectra were recorded from 25 individual cells (cytoplasm regions) and exported as machine-readable files (.csv). MATLAB scripts are provided to implement two-dimensional correlation spectroscopy (2D-COS) for generating synchronous maps, along with utilities for loading spectra, averaging, and reproducing key figures. A set of simulated time-series spectra used to validate 2D-COS under high background …

Application Of Hyflex In The Application Security Module, Vanessa Ayala-Rivera

Case studies: Digital Education

No abstract provided.

An Evening With Mobile Hyflex, Peter Alexander

Case studies: Digital Education

Network Security is a 10-credit module taught on the part-time Bachelor of Science in Computing in Digital Forensics & Cyber Security course in TU Dublin. While the overall course is mainly delivered online, there are some topics in this particular module which benefit from having a hands-on interactive element. The challenge though with facilitating learners to have that interactive experience is that the ones who cannot travel to campus should not be excluded. The mobile Hyflex project helped address this challenge by giving students both on campus and online a comparable interactive experience. Changes made to practice (100-150 words).

Shrinkage Study In Photopolymerisable Hybrid Sol-Gel Through Holographic Patterning, Jamshed Aftab, Izabela Naydenova, Tatsiana Mikulchyk

Articles

Photopolymerisation induced shrinkage of holographic materials is one of the main factors which needs to be considered for designing holographic optical elements (HOEs) with high accuracy in light redirection with maximum efficiency. This work studies the shrinkage in photopolymerisable hybrid sol-gel (PHSG) by examining the properties of volume transmission gratings recorded in PHSG layers. It explores both the dependence of shrinkage on the holographic grating parameters (thickness, spatial frequency, slant angle) and the effect of material aging. By using the fringe-plane rotation model, shrinkage is found to have the maximum value of 1.37 % at 765 lines/mm (19.36° slant angle) …

Quantum Machine Learning For Battery Health And Thermal Risk Prediction, Alexander Mutiso Mutua, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

The rapid growth of connected Electric Vehicles (EV) as part of modern Intelligent Transport Systems (ITS) motivates the need for real-time management of Lithium-ion (Li-ion) battery health and thermal risks. Li-ion batteries, although widely used, are prone to degradation and thermal runaway, posing significant challenges for safe and efficient EV operation. We present a Quantum Machine Learning (QML) and Agent-Based Model (ABM) that simulates and predicts EV behaviour under various battery degradation con- ditions. We use a Variational Quantum Neural Network (VQNN) trained on NASA battery datasets to classify EVs into four cate- gories: healthy, degraded for fixed chargers, degraded …

Statistical Programming For Adaptive Monitoring In Software Defined Networks Using Linear Programming, Fatemeh Amou Aghaei, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

Adaptive monitoring in Software Defined Networks (SDNs) is essential to reduce overhead and prioritize critical flows. This paper introduces AdaptMon, a Linear Programming-based model that dynamically allocates monitoring resources based on estimated error rates. By modeling allocation as a probability distribution and enforcing a fairness constraint using an ℓ1-style deviation bound, the approach maximizes expected monitoring utility while preserving balance across the network. Simulations show that AdaptMon reduces monitoring delay by up to 40% without sacrificing anomaly detection accuracy. The model is interpretable, lightweight, and grounded in statistical programming, making it a practical solution for real-time SDN environments.

A Transfer Learning Load Adjusted Approach For Video-On-Demand Systems Given Limited Training Data, Kangogo Kimeli, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

Inadequate data complicates planning and allocation of VoD resources, potentially hindering the scalability of VoD services. We propose a Transfer Learning Load Adjusted (TLLA) algorithm for resource management given limited VoD data. TLLA leverages the knowledge gained from pre-trained models by storing features and patterns that can be used to train Machine Learning (ML) related tasks. We model limitations in VoD data by proportionally freezing 50% of the neural layers in models trained from pre-trained and source domains. We evaluate the performance of the frozen neural layers by comparing them to unfrozen data. Freezing 50% of the neural layers in …

Shape-Based Nanoparticle Classification Using Machine Learning, Caitlin Caitlin Robertson, Hender Lopez

SAML-25 Workshop on Statistical and Machine Learning

The accurate classification of nanoparticles (NPs) based on their shapes is crucial for understanding their physical-chemical properties and predict their bioactivity. Nowadays, synthesis method are able to produce a broad range of shapes, such as spheres, cubes and branched NPs and commonly these NP shapes are only described qualitative. This study presents NP descriptors obtained from NPs contours extracted from electron microscopy images. Descriptors such as Fourier descriptors, aspect ratio, and compactness are then used as input for machine learning classifiers. In particular, XGBoost, Random Forest, and neural networks are explored and the their performances are compared and discussed.

Enhancing Dermatological Skin Lesion Classification With Multi-Modal Attention-Based Models And Explainability, Conan Oreilly

SAML-25 Workshop on Statistical and Machine Learning

Accurate classification of skin lesions is critical for early detection of melanoma and other malignancies, particularly in resource-limited settings. This study presents a novel multi-modal machine learning framework that integrates dermoscopic images and structured clinical metadata to improve diagnostic performance. Leveraging the PAD-UFES-20 dataset, which includes over 2,000 smartphonecaptured lesion images and associated patient metadata, we benchmark a series of unimodal and multimodal models. Our results demonstrate that modality attention fusion (MAF) applied to a frozen SwinV2-Tiny vision transformer and metadata multi-layer perceptron (MLP), augmented with focal loss, yields a state-ofthe- art weighted F1-score of 0.84 and balanced accuracy of …

Analyzing Option Chain Bid–Ask Spreads With Machine Learning, Brian Byrne, Qianru Shang

SAML-25 Workshop on Statistical and Machine Learning

This paper investigates the determinants of option bid–ask spreads using machine learning techniques. We analyze a cross-sectional dataset of Apple Inc. (AAPL) call options, focusing on the relative bid–ask spread as the target variable. By comparing linear models with ensemble methods such as Random Forests and XGBoost, we find that nonlinear machine learning methods significantly outperform traditional OLS regression. The most influential factors are moneyness, implied volatility, and time to expiration, while volume and open interest have limited predictive power. Results suggest that spreads are driven by a mix of market microstructure dynamics, capital constraints, and regulatory requirements such as …

Intention To Commute By Public Transportation And/Or By Foot: Findings From A Pls Structural Equation Model, Simona Balzano, Houyem Demni,, Edoardo Pascucci,, Luisa Natale, Giuseppe Cappelli, Sofia Nardoianni, Giovanni C. Porzio

SAML-25 Workshop on Statistical and Machine Learning

Sustainable mobility stands at the forefront of contemporary discussions, driven by the clear imperative to transition towards more environmentally friendly transportation and patterns. This shift is widely recognized as a crucial opportunity to address the challenges and inherent dangers posed by climate change. It is then crucial to introduce attitudes to encourage voluntary behavioral changes toward different sustainable solutions. In this perspective, to foster a future where sustainable personal mobility options are widely embraced and integrated, it is crucial to comprehend the inclination of younger generations to use them. For this reason, a survey on the use of sustainable mobility …

Shedding Light On Cellular Glycolysis Pathway Kinetics Using A Spectralomics Approach, Integrating Multivariate Statistical And Machine Learning Analytical Approaches, Nitin Patil, Zohreh Mirveis, Hugh Byrne

SAML-25 Workshop on Statistical and Machine Learning

The potential of time resolved label-free Raman microspectroscopy to elucidate the kinetics of cellular and subcellular glycolysis pathway was explored in this study. A549, human lung cells were cultured in an unbuffered minimal medium with glucose as a sole carbon source under three different modulated conditions. Modulator drugs oligomycin and 2-deoxyglucose were used to stimulate and inhibit the glycolysis pathway. Initially the kinetic glycolysis assay was used to monitor the glycolysis end-point kinetics followed by development of a numerical model capable of simulating the end-point kinetics. For Raman spectroscopy, samples at different timepoints from the experiments with similar conditions as …

Ai-Driven Personalized Radiotherapy Planning, Nithin Venkatesh, Marco Pota, Maged Shaban

SAML-25 Workshop on Statistical and Machine Learning

The planning of radiation oncology treatment is made more dynamic and individualized by Artificial Intelligence (AI). Routine radiotherapy practice applies normative procedures indifferent to patient-specific parameters such as tumor volume, patient anatomy, and heterogeneity in the delineation of treatment response. Inadequate and over-radiation treatment is the most prevalent outcome. Further, with the inclusion of AI, it can facilitate enhancing the healthcare industry through optimizing radiotherapy using an array of patient information such as molecular profiles and imaging data. The product offers an end-to-end AI-driven solution to all aspects of radiotherapy, from initial consultation (diagnosis) to adaptive treatment planning. All the …

Early Lane Change Prediction For Mixed Traffic With V2x Communication, Muhammed Fatih Koc, Nouman Ashraf, Pramod Pathak, Sachin Sharma

SAML-25 Workshop on Statistical and Machine Learning

Lane change prediction is essential for ensuring road safety and effective decision-making in autonomous vehicles (AVs). AVs will probably take several decades to penetrate new vehicle sales. As AVs and human-driven vehicles (HDVs) will coexist in traffic for the long term, AVs must understand the lane change intentions of surrounding HDVs. Lane changing is a critical manoeuvre that can cause a crash if it is performed late or if incorrect lane adjustments are made. Therefore, forecasting surrounding vehicles’ lane change intentions in advance is essential to ensure safe driving in mixed traffic environments having both AVs and HDVs. The unpredictability …

A Machine Learning Approach To Improve Prediction In Chemical Exposure Risk Assessment, Michele Marro, Cédric Koller, Hasnaa Chettou, David Vernez

SAML-25 Workshop on Statistical and Machine Learning

Exposure models play a crucial role in predicting chemical exposure in workplaces, offering an essential alternative to measurements, which are resource-intensive and time-consuming and sometimes not possible. Despite their widespread use and continuous development, significant challenges persist, including variability in predictions, limited model updates, and difficulties in accessing the required input data. In this study, we investigate how modern machine learning techniques can contribute to the improvement of exposure models by addressing these limitations. To overcome the frequent lack of data, we explore the use of synthetic datasets generated through existing exposure models. This approach allows for the study of …

Interpretable Ai In Education: A Comparison Of Glass-Box Models For Predicting Student Success, Jan Glazenborg

SAML-25 Workshop on Statistical and Machine Learning

This Master’s thesis addresses early identification of first-year Computer Science students at risk of underperformance by comparing inherently interpretable (“glass-box”) predictive models with the existing Naïve Bayes–based PreSS tool. The PreSS dataset was originally compiled by Quille & Bergin from 692 first-year CS1 students across eleven institutions in Ireland and Denmark, who completed surveys on programming and mathematics backgrounds, gaming habits and a short programming test four to six hours into the course. Seventeen normalized features capturing demographic, academic and behavioural factors were extracted. In this thesis, four machine learning models are evaluated: Naïve Bayes, explainable boosting machines, automatic piecewise …

Pros & Cons Of Reinforcement Learning - Illustrated By The Problem Of Controlling Gantry Robots, Horst Zisgen

SAML-25 Workshop on Statistical and Machine Learning

In this talk a solution for the dynamic scheduling of flexible flow shop systems using gantry robots for material handling by means of simulation and Reinforcement Learning (RL) is presented. Subsequently the pros and cons of a RL approach are briefly discussed and illustrated at the robot control problem.