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What is the number one persuasive \"tool\" or message that can make you act on something and why? Solution What is the number one persuasive \"tool\" or message that can make you act on something and why? Reciprocation and appreciation are the biggest persuasvie tools that make be act on something right away. For example, if I know or believe that I will be reciprocated financially for performing a task or job well, I become motivated and act on it. Also, if I believe there is appreciation for completion of the job or task, I am equally motivated. I think these are the two biggest factors that determine whether I take action or not in a work environment. When these two things are present, I receive personal fulfillment in doing a job or task and that makes all the difference in the world. .

What is the number one persuasive "tool" or message that can make you act on something and
why?
Solution
What is the number one persuasive "tool" or message that can make you act on
something and why?
Reciprocation and appreciation are the biggest persuasvie tools that make be act on something
right away. For example, if I know or believe that I will be reciprocated financially for
performing a task or job well, I become motivated and act on it. Also, if I believe there is
appreciation for completion of the job or task, I am equally motivated. I think these are the two
biggest factors that determine whether I take action or not in a work environment. When these
two things are present, I receive personal fulfillment in doing a job or task and that makes all the
difference in the world.

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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.

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