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              <text>This project report explores the base sizes of group actions. We begin with some preliminary definitions and results. Next, we investigate a combinatorial approach to proving the bound $|G| \geq 2^{b(G)}$; we give a conjecture for a stronger statement concerning a system of distinct representatives for sets $\left\{ \bigcap\limits_{\alpha \in \Omega}\mathrm{Stab}_G(\alpha) : \emptyset \neq \Omega \subseteq B \right\}$, where $B$ is a minimal base for a permutation group $G$.  Subsequently, we follow a paper of Burness, O'Brien and Wilson in explaining various computational and character-theoretic techniques that can be used to compute base sizes. Finally, we apply these methods to a selection of almost simple sporadic groups and their subgroups.</text>
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                <text>Eleanor Milburn </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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              <text>Imagine that when a virus enters our body, our immune system is activated, and some cells start to track and kill the virus. Here is an interesting question: How do the cells know which direction is the correct one, given that the movement of the virus is totally random?  My project will use differential equations, particularly stochastic differential equations, to explain this phenomenon and numerical methods that rely on computers to predict the potential future movement of the cells.</text>
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                <text>Differential Equations in Cell Motility: Applications of Numerical Methods in the Real World</text>
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              <text>How can we accurately detect and locate a modification on an RNA molecule using nanopore sequencing data? This is an open problem that requires ideas from both statistics and biology. Supervised by Professors Anastasia Papavasiliou and Jon Forster, I began by simulating the signal data from Oxford Nanopore devices, where a sliding window reads five bases on the mRNA chain at a time. Using R, we constructed a statistical framework which involves working with different distributions such as poisson, inverse gamma and the normal distribution to generate signal values. Then we analysed our signal values, performing hypothesis tests and calculating maximum likelihood scores in order to determine where modifications are likely to be on the mRNA chain. This year, after gaining valuable biological insight at the Laboratory of Immune Diversity in the German Cancer Research Centre in Heidelberg, I have created a greedy search algorithm to maximise our likelihood score and detect RNA modifications from the signal data. To improve the accuracy, we included a posterior likelihood calculation into our score and I also coded a birth-death algorithm. Then, I tested both methods with multiple reads (signal lists) per mRNA type which proved to be more accurate and created interesting results!</text>
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              <text>Stochastic differential equations (SDEs) and multiscale models are key tools in modern mathematical modelling. They enable us to accu- rately and efficiently analyse real world systems that exhibit randomness and behaviour across different scales of space and time. This review paper ex- plores the foundational physical principles behind them: Brownian motion, which serves as a cornerstone of stochastic analysis, and fluid mechanics, a prominent example of a multiscale system.</text>
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