Nata sample papers

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Mosaic Institute of Design provides Nata Sample Papers for Nata Aspirants to prepare for Nata Entrance Exam 2014-15. To purchase Nata previous year question papers set, containing 100 papers or full Nata Study Material visit http://www.mosaicdesigns.in/study-material/NATA-Study-Material Nata Study Material Highlights 1.The Nata full course package contains 10 Nata Preparation Books & a CD covering all the topics for Nata Online Test & Nata Drawing Test. 2.Nata Mini Course package also available. 3.Order online and get Nata Study Material delievered right at your doorstep. 4.Get the benefits of expert online support & guidance. To view successful Nata results by Mosaic Institute Of Design visit http://www.mosaicdesigns.in/results/NATA •Classroom Coaching Options at Mosaic : Nata Regular Coaching / Nata Crash Courses / Nata Postal Cum Classroom Coaching. •Admission Open For : March 2014 / April 2014 / May 2014 / June 2014 / July 2014 •Who can apply : 9th / 10th / 11th / 12th appearing / passed students. For More Details Contact: Mosaic Institute Of Design 71-73, Ist Floor, Sardar Centre, Opp. Vastrapur Lake, Vastrapur, Ahmedabad – 380015 Ph: 09824083847, 09924082625 Visit: www.mosaicdesigns.in or mail info@mosaicdesigns.in

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Today there are several guides available in the market for mathematics and science subjects but none for architecture aptitude tests covering the syllabus of NATA . These Sure-shot NATA SAMPLE/GUESS PAPERS have been prepared to bridge the gap especially for those candidates who have no access for personal coaching. It took many years to complete this handbook and hundreds of students were interacted before putting this handbook into final shape. These Sure-shot NATA SAMPLE/GUESS PAPERS prepare potential entrants for success in obtaining merit in the aptitude test. Full care has been taken to cover the entire syllabus of NATA.

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The file is a sample of the full study material provided by PRECEPT Design Studio. The full study material covers both Theory and drawing module for NATA with topics like Perspective drawing, 3D Composition, 2D Composition, Logo Design, 3D Visualization, Building Material Basics, Arithmatic Aptitude, Architectur eAwareness, Logical Reasoning, History of Architecture, Color Theory, etc. along with some solved NATA Drawing Questions.

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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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