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              <text>Students and teachers often struggle to find past paper questions that align with specific syllabus topics. GCSE and A-Level Computer Science exams from AQA, Edexcel, and OCR provide a wide range of practice materials, but these are typically organised by year and exam board rather than by topic, requiring students to manually sift through entire papers to locate relevant questions and reducing the time available for effective revision. This project aims to develop an automated system that classifies and labels past paper questions according to their syllabus topics, focusing initially on OCR GCSE Computer Science papers. By applying Natural Language Processing (NLP) techniques such as TF–IDF vectorisation, Support Vector Machine (SVM) classification, and data augmentation, the system is able to automatically tag each question with its corresponding syllabus code, while trials with transformer-based models like BERT and DistilBERT explore their potential for educational question classification. The resulting labelled and searchable database improves accessibility of past papers for targeted revision and has practical implications for students, teachers, and educational platforms, while also laying the groundwork for future AI-driven educational tools such as automated worksheet generation and personalised learning systems.</text>
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                <text>Molecules, just like many objects, can exist in structurally iden�cal but mirror image forms known as ‘enan�omers’ (Figure 1 – like &#13;
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              <text>Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by persistent synovial inflammation, joint destruction, and pain. While immune mechanisms underlying RA are well established, emerging evidence highlights an important role for neuro-immune crosstalk in disease progression. In particular, mast cells and transient receptor potential (TRP) ion channels may contribute to inflammatory signalling within the synovium. This study aimed to genotype and characterise transgenic mouse models of RA to investigate the roles of mast cells and TRP channels, with a focus on TRPM2 and TRPM8.  Wild-type, heterozygous, conditional knockout, and global knockout mice were analysed. Genotyping was performed using PCR and agarose gel electrophoresis to confirm genotype. Histological analysis of mouse knee joints was conducted using haematoxylin and eosin staining to assess tissue morphology, toluidine blue staining to identify mast cells, and immunostaining to detect TRP ion channel expression. Protein analysis was performed using ELISA to quantify inflammatory mediators, including IL-6, PGE₂, and Substance P, alongside Bradford assays to determine total protein content.  PCR successfully confirmed the generation of transgenic mouse lines. Histological analysis revealed synovial inflammation and mast cell infiltration characteristic of RA, while immunostaining identified TRPM2 and TRPM8 expression within inflamed joints. TRPM2 knockout mice exhibited reduced inflammatory features compared to wild-type controls. ELISA confirmed the presence of key inflammatory mediators, supporting a mechanistic link between neuronal signalling and mast cell-driven inflammation. Collectively, these findings support a contributory role for mast cells and TRP ion channels in RA pathology and highlight their potential as therapeutic targets.</text>
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              <text>Under the supervision of Dr Gareth Tracey, I have completed an undergraduate research project studying base sizes of permutation groups. I am interested in computational and character-theoretic techniques used to obtain these base sizes. I have computed several base sizes for almost simple sporadic groups and their subgroups, extending results in the literature. I am also interested in applications of combinatorial techniques to general results about base sizes. I have explored an application of Hall’s Marriage Theorem to take steps towards a new proof of an existing result. </text>
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