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Poster to be presented at Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, Kaust, Saudi Arabia, https://cemse.kaust.edu.sa/stochnum/events/event/snsl-workshop-2024. In this work we have considered a setting that mimics the Henry problem \cite{Simpson2003,Simpson04_Henry}, modeling seawater intrusion into a 2D coastal aquifer. The pure water recharge from the ``land side'' resists the salinisation of the aquifer due to the influx of saline water through the ``sea side'', thereby achieving some equilibrium in the salt concentration. In our setting, following \cite{GRILLO2010}, we consider a fracture on the sea side that significantly increases the permeability of the porous medium. The flow and transport essentially depend on the geological parameters of the porous medium, including the fracture. We investigated the effects of various uncertainties on saltwater intrusion. We assumed uncertainties in the fracture width, the porosity of the bulk medium, its permeability and the pure water recharge from the land side. The porosity and permeability were modeled by random fields, the recharge by a random but periodic intensity and the thickness by a random variable. We calculated the mean and variance of the salt mass fraction, which is also uncertain. The main question we investigated in this work was how well the MLMC method can be used to compute statistics of different QoIs. We found that the answer depends on the choice of the QoI. First, not every QoI requires a hierarchy of meshes and MLMC. Second, MLMC requires stable convergence rates for $\EXP{g_{\ell} - g_{\ell-1}}$ and $\Var{g_{\ell} - g_{\ell-1}}$. These rates should be independent of $\ell$. If these convergence rates vary for different $\ell$, then it will be hard to estimate $L$ and $m_{\ell}$, and MLMC will either not work or be suboptimal. We were not able to get stable convergence rates for all levels $\ell=1,\ldots,5$ when the QoI was an integral as in \eqref{eq:integral_box}. We found that for $\ell=1,\ldots 4$ and $\ell=5$ the rate $\alpha$ was different. Further investigation is needed to find the reason for this. Another difficulty is the dependence on time, i.e. the number of levels $L$ and the number of sums $m_{\ell}$ depend on $t$. At the beginning the variability is small, then it increases, and after the process of mixing salt and fresh water has stopped, the variance decreases again. The number of random samples required at each level was estimated by calculating the decay of the variances and the computational cost for each level. These estimates depend on the minimisation function in the MLMC algorithm. To achieve the efficiency of the MLMC approach presented in this work, it is essential that the complexity of the numerical solution of each random realisation is proportional to the number of grid vertices on the grid levels.

$Poster_density_driven_with_fracture_MLMC.pdf$ $Poster_density_driven_with_fracture_MLMC.pdf$

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APM webinar hosted by the Scotland Network on 14 May 2024. Speakers: Chris Drysdale and Peter Huggett An interactive session discussing how Project Managers can identify mental health symptoms, provide tools to help themselves and others, plus also increase the capabilities of the Project Management function. This webinar was held on 14 May 2024. The covid-19 pandemic led to concerns about a worsening of mental health & wellbeing across the world and increased awareness in both society and the workplace. This webinar looks to advise the benefits of having a Mental Health First Aid function in the workplace whilst also providing tools and techniques that can be readily used and applied to yourself and colleagues. Additionally, there are wider benefits to Project Management which will be proposed and discussed.

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Orthodontic treatment involves a delicate balance of science and artistry, where precision is paramount for optimal outcomes. Join us for an enlightening presentation as we explore the nuanced concept of the "Envelope of Discrepancy" within the realm of orthodontics. In this session, we'll embark on a comprehensive journey through the challenges and intricacies of managing discrepancies in orthodontic diagnosis and treatment planning. From assessing dental and skeletal irregularities to understanding soft tissue dynamics, we'll dissect the multifaceted nature of discrepancies and their implications for treatment efficacy. Drawing upon the latest research and clinical insights, we'll unveil practical strategies and innovative techniques aimed at optimizing treatment outcomes while minimizing discrepancies. Whether you're a seasoned orthodontist or a budding practitioner, this presentation promises invaluable knowledge and actionable insights to elevate your practice and enhance patient satisfaction.

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$Poster_density_driven_with_fracture_MLMC.pdf$ $Poster_density_driven_with_fracture_MLMC.pdf$

Poster_density_driven_with_fracture_MLMC.pdf

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