44 10 851 https://urss.warwick.ac.uk/files/original/d28dff1ea248ef7133e55554f7b5d019.pdf 9a19872ba7d2d92644a4644f829c4ecc Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Poster An academic poster Abstract The abstract of the paper Removal of line defects with winding number larger than 1 in cholesteric liquid crystals can lead to some interesting arrangements of different characterisations of areas within the liquid crystal. Meta Tags Meta tags liquid crystals, cholesterics, physics, soft matter, line defects, contact topology, optically anisotropic materials Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource James Rampton Contributor An entity responsible for making contributions to the resource Gareth Alexander Subject The topic of the resource Physics Title A name given to the resource Line Defects in Cholesteric Liquid Crystals with Large Winding Number Date A point or period of time associated with an event in the lifecycle of the resource 2024 https://urss.warwick.ac.uk/files/original/a679fa2a218377b5abc38721fa7433ac.pdf 1b03331be23c1ace167d1e17906e3ca9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Mathematics Academic Poster An academic poster Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Lines on Non-Singular Cubic Surfaces Subject The topic of the resource Mathematics Creator An entity primarily responsible for making the resource Jack Lucas Date A point or period of time associated with an event in the lifecycle of the resource 2023 Algebraic Geometry Tag Algebraic Surfaces Intersection Theory Tag Jack Lucas Mathematics Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Hyperlink A link, or reference, to another resource on the Internet. URL https://abelaineladinez.wixsite.com/abbysurssblog Meta Tags Meta tags Development, Centrosomes, Localisation, C. elegans, Molecular Biology Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Localising Centrosomal Proteins During Embryogenesis Description An account of the resource Blog documenting my experience within a developmental biology lab, exploring new techniques and molecular biology skills. Creator An entity primarily responsible for making the resource Abelaine Ladinez Subject The topic of the resource Life Sciences Date A point or period of time associated with an event in the lifecycle of the resource 2025 https://urss.warwick.ac.uk/files/original/acfbfb30df2e05deeb3d24055c40868b.pdf a8ff62ed5fc99ee9b14fca0d4bbecc2d Academic Poster An academic poster Abstract The abstract of the paper Boron nanoclusters can form planar, cage-like, open and compact structures because electron-deficient multicentre bonding produces many low-energy arrangements. Identifying stable structures means the use of a large number of geometry optimisations, making direct screening with density functional theory computationally expensive. In this project, 125 AIRSS-generated candidates for each cluster size from B₁ to B₂₀₀ were independently optimised using the 7NET, MACE and PET-MAD machine-learning interatomic potentials, giving 75,000 optimisation attempts. The lowest-energy converged structure from each model and cluster size was selected, producing 600 structures for analysis. PET-MAD achieved the highest overall convergence at 99.83%, followed by 7NET at 99.44% and MACE at 96.92%. 7NET generally produced the most compact, highly coordinated structures, while PET-MAD gave the closest qualitative comparison with an independent VASP-DFT B₅₃ reference. These results show that MLIPs can efficiently screen boron nanoclusters, although their predictions remain model-dependent and require matched DFT calculations for rigorous validation. Meta Tags Meta tags Chemistry, Computational, Boron Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Compared with DFT: Streamlining Boron-Cluster Simulations Creator An entity primarily responsible for making the resource Mr. Daniel Lowe Contributor An entity responsible for making contributions to the resource Dr. Bora Karasulu Subject The topic of the resource Boron Nanoclusters https://urss.warwick.ac.uk/files/original/00e4bd2477baa132f3ef9d14de95f331.pdf cda88e29574a7eea799d69e1eede4e15 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Paper An academic paper Abstract The abstract of the paper This research project aims to comprehensively implement and evaluate several machine learning models for the critical task of brain tumour classification using Magnetic Resonance Imaging (MRI) data. A classical Support Vector Machine (SVM) will serve as a baseline, against which the performance of advanced deep learning architectures, including Convolutional Neural Networks (CNNs) and a Bayesian Convolutional Neural Network (BCNN), will be assessed. The study will rigorously compare these models across key performance indicators: classification accuracy, the robustness of uncertainty quantification, and computational efficiency. Ultimately, this work seeks to provide valuable insights into the efficacy and utility of diverse machine learning paradigms in medical image analysis, with a particular focus on the crucial role of uncertainty estimation in supporting more reliable and informed decisions for early brain tumour diagnosis. Meta Tags Meta tags Mathematics, Machine Learning, Statistics, Computer Vision, Deep Learning, Bayesian, Medical Imaging Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Date A point or period of time associated with an event in the lifecycle of the resource 2005 Title A name given to the resource Machine Learning for Brain Tumour Detection: Comparing Probabilistic, Classical, and Deep Learning Models Subject The topic of the resource Statistics Creator An entity primarily responsible for making the resource Ignacio Vigil Cardenal https://urss.warwick.ac.uk/files/original/09133909c52324f6b0eb0f46165d9f6d.pdf 60464a7beec2a864041df5df3b4c0dc3 Academic Poster An academic poster Abstract The abstract of the paper If everything is empty, why be compassionate? Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Madhyamaka Buddhist Meta-ethics Subject The topic of the resource Philosophy Creator An entity primarily responsible for making the resource Cameron Milne Date A point or period of time associated with an event in the lifecycle of the resource 2023 buddhism compassion Madyhamaka Philosophy https://urss.warwick.ac.uk/files/original/3d1bccae82ca6d8d43579ad70eeb9e76.pdf 2108c63738fea5a4b4afd6353c9d378e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Paper An academic paper Abstract The abstract of the paper This research explores the potential of Maglev technology as a sustainable transportation solution, with a focus on its alignment with SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Using the Shanghai Maglev as a case study, the paper highlights the system’s energy efficiency, reduced carbon emissions, and low noise pollution, which contribute to environmental sustainability. The electromagnetic suspension (EMS) and electrodynamic suspension (EDS) systems eliminate friction, resulting in significant energy savings compared to conventional rail systems. While the technology presents considerable benefits, including low operational and maintenance costs, its widespread adoption faces challenges due to high infrastructure costs and the complexity of integrating it with existing transit networks. Despite these hurdles, Maglev has the potential to be powered by renewable energy, further reducing its environmental impact. This paper argues that Maglev technology, with its longterm sustainability benefits, represents a viable alternative for future urban and intercity transportation, particularly in areas where traditional transit systems are insufficient to meet growing sustainability targets. Meta Tags Meta tags Maglev, Magnetic Levitation, Sustainable Transportation, Environmental Sustainability, SDG 11, SDG 13, Climate Action, Energy Efficiency, Renewable Energy, Green Technology, High-Speed Rail, Electromagnetic Suspension (EMS), Electrodynamic Suspension (EDS), Low Carbon Emissions, Noise Pollution Reduction, Urban Transportation, Sustainable Cities, Future Mobility Solutions, Maglev Trains, Public Transit Innovation. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Subject The topic of the resource Engineering Date A point or period of time associated with an event in the lifecycle of the resource 2024 Title A name given to the resource Magnetizing Transportation: Exploring the Viability of Maglev for Sustainable Travel Creator An entity primarily responsible for making the resource Gagandeep Singh Kapoor https://urss.warwick.ac.uk/files/original/1e476668d9dd20f071c41ba049fc0a4c.pdf 02fd145dee3ce3e63382f927dbcc9fef Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Poster An academic poster Abstract The abstract of the paper Molecules relax to their most stable, lowest-energy structures through geometry relaxation. While DFT provides accurate results, it is computationally expensive. This work compares machine learning interatomic potentials (MLIPs) to DFT, evaluating how well MLIPs reproduce DFT-level stability at a fraction of the cost. Meta Tags Meta tags Computational Chemistry, DFT, Density Functional Theory, MLIP, VASP, Atomic Simulations Environment, CHGNet, MACE, SevenNet Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ashan Balasuntharam Subject The topic of the resource Chemistry Title A name given to the resource Making Molecules Stable: How AI Helps in Relaxations Date A point or period of time associated with an event in the lifecycle of the resource 2025 https://urss.warwick.ac.uk/files/original/67aa51ec21484f92cb72bbc22946aae5.pdf 36ce2dd9bc86c3f10d949c2f04671a02 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Social Sciences Subject The topic of the resource Social Sciences Academic Poster An academic poster Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Malaysian Youth: The Ethnicization of Politics and its Impacts Subject The topic of the resource Sociology Creator An entity primarily responsible for making the resource Myra Tan Date A point or period of time associated with an event in the lifecycle of the resource 2023 EastAsia Education Ethnonationalism Globalisation identity Malaysia Myra Tan nationalism Politics Race Religion Sociology SoutheastAsia youth https://urss.warwick.ac.uk/files/original/7eb9a76bfd57a79879ed1ae4e3df9e20.pdf d67a086738cec2f944e973c9e4349867 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Poster An academic poster Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Mapping Motor Protein Activator Sites Subject The topic of the resource Warwick Medical School Creator An entity primarily responsible for making the resource Hazel Hassan Date A point or period of time associated with an event in the lifecycle of the resource 2022 cellular kinesin primers. PCR Protein