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              <text>Unsupervised machine learning clustering algorithms can be used in a variety of different ways to segment a market. This study uses such algorithms to perform benefit based market segmentation, grouping consumers of small businesses in the United States (US) into 3 segments based on how much they value various factors characterising a small business and its online presence. A recent method was used to decide how to apply clustering algorithms to our dataset. For a given algorithm, this method chooses the number of clusters to ensure that the clustering created is stable; however running the algorithm on individual clusters created by the first run of the algorithm produces unstable clusters.</text>
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              <text>This report contains our research output after participating in the 2025 Undergraduate Research Support Scheme (URSS) at the University of Warwick. The project was carried out with the Warwick Mathematics Institute (WMI) under the supervision of Helena Verrill. First, we introduce important concepts that we then use to study some of the properties of the stereographic projection. Using these properties, we investigate how this projection redistributes lattice points by extending Minkowski's theorem to sets on the sphere. We finish our project by developing and 3D printing models of the stereographic projection.</text>
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              <text>There are major differences in the regulation of genes in 2D and 3D cell cultures. Through RNA sequencing analysis, this project inspects some of these differences in Epstein-Barr Virus (EBV) positive Nasopharyngeal Carcinoma (NPC) cultures, particularly demonstrating a downregulation in key regulators of the cell cycle such as the Anaphase Promoting Complex (APC/C) and Origin Recognition Complex (ORC) in the 3D models. This is evidence that 3D models are more representative of tumour microenvironments and therefore supports their use in pre clinical studies for improved downstream analysis. </text>
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                <text>Under the supervision of Dr Matthias Caro, I studied the fundamentals of quantum information theory and quantum cryptography. Drawing inspiration from the structure of the BB84 protocol, this project develops a novel framework for certifying quantum entanglement through local, fully classical measurements: an approach that, while conceptually minimal and elegant in retrospect, has not been formally characterised or rigorously analysed in the literature. The work establishes a tolerant identity test for arbitrary maximally entangled states, extends the protocol to multi-pair systems, and investigates its robustness in the presence of noise.</text>
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