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A dynamical study of saline plumes in desalination outfalls
No full texthttp://dx.doi.org/10.1017/S000497271200033
Using administrative data to gain insights into microdrivers of productivity
No full texthttp://dx.doi.org/10.1017/S000497271200033
A gradient recovery approach for nonconforming finite element methods with boundary modification: Gradient Recovery Approach for Non-Conforming Finite Element
No full textWe use orthogonal and biorthogonal projections to post-process the gradient of the finite element solution produced by a non-conforming finite element approach. This leads to a better approximation property of the recovered gradient. We use an L2-projection, where the trial and test spaces are different but form a biorthogonal system. This leads to an efficient numerical approach. We also modify our projection by applying the boundary modification method to obtain a higher order approximation on the boundary patch. Numerical examples are presented to demonstrate the efficiency and optimality of the approach.
References
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B. P. Lamichhane, R. P. Stevenson, and B. I. Wohlmuth. Higher order mortar finite element methods in 3D with dual Lagrange multiplier bases. Numer. Math. 102 (2005), pp. 93–121. doi: 10.1007/s00211-005-0636-z
A. Naga and Z. Zhang. A posteriori error estimates based on the polynomial preserving recovery. SIAM J. Numer. Anal. 42.4 (2004), pp. 1780–1800. doi: 10.1137/S0036142903413002
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Z. Zhang and A. Naga. A new finite element gradient recovery method: Superconvergence property. SIAM J. Sci. Comput. 26.4 (2005), pp. 1192–1213. doi: 10.1137/S1064827503402837
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Elasticity equations with random domains—the shape derivative approach
Get PDFIn this work, we discuss elasticity equations on a two-dimensional domain with random boundaries and we apply these equations to modelling human corneas.
References
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F. Ballarin, A. Manzoni, G. Rozza, and S. Salsa. Shape optimization by free-form deformation: Existence results and numerical solution for Stokes flows. J. Sci. Comput. 60.3 (2014), pp. 537–563. doi: 10.1007/s10915-013-9807-8.
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J. Dick. Higher order scrambled digital nets achieve the optimal rate of the root mean square error for smooth integrands. Ann. Statist. 39.3 (2011), pp. 1372–1398. doi: 10.1214/11-AOS880.
J. Dick, F. Y. Kuo, Q. T. Le Gia, and Ch. Schwab. Multilevel higher order QMC Petrov–Galerkin discretization for affine parametric operator equations. SIAM J. Num. Anal. 4.54 (2015), pp. 2541–2568. doi: 10.1137/16M1078690
A. Eilaghi, J. G. Flanagan, I. Tertinegg, C. A. Simmons, G. W. Brodland, and C. R. Ethier. Biaxial testing of human sclera. J. Biomech. 43 (2010), pp. 1696–1701. doi: 10.1016/j.jbiomech.2010.02.031.
U. Fares, A. M Otri, M. A. Al-Aqaba, and H. S. Dua. Correlation of central and peripheral corneal thickness in healthy corneas. Cont. Lens Anterior Eye 35 (2012), pp. 39–45. doi: 10.1016/j.clae.2011.07.004.
R. N. Gantner and Ch. Schwab. Computational higher order quasi-Monte Carlo integration. Monte Carlo and quasi-Monte Carlo methods. Vol. 163. Springer Proc. Math. Stat. Springer, 2016, pp. 271–288. doi: 10.1007/978-3-319-33507-0_12. on p. C129).
H. Harbrecht. Second moment analysis for Robin boundary value problems on random domains. Singular Phenomena and Scaling in Mathematical Models. Ed. by M. Griebel. Springer, 2014, pp. 361–381. doi: 10.1007/978-3-319-00786-1_16.
H. Harbrecht, M. Peters, and M. Siebenmorgen. Analysis of the domain mapping method for elliptic diffusion problems on random domains. Numer. Math. 134.4 (2016), pp. 823–856. doi: 10.1007/s00211-016-0791-4.
H. Harbrecht, R. Schneider, and Ch. Schwab. Sparse second moment analysis for elliptic problems in stochastic domains. Numer. Math. 109.3 (2008), pp. 385–414. doi: 10.1007/s00211-008-0147-9.
R. Hiptmair and J. Li. Shape derivatives in differential forms I: an intrinsic perspective. Ann. Matematica Pura Appl. 192 (2013), pp. 1077–1098. doi: 10.1007/s10231-012-0259-9.
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R. Martin, S. Jonuscheit, A. Rio-Cristobal, and M. J. Doughty. Repeatability of Pentacam peripheral corneal thickness measurements. Cont. Lens Anterior Eye 38 (2015), pp. 424–429. doi: 10.1016/j.clae.2015.05.001.
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F. Orucoglu and E. Toker. Comparative analysis of anterior segment parameters in normal and keratoconic eyes generated by Scheimpflug tomography. J. Ophthalmol., 925414 (2015), pp. 1–8. doi: 10.1155/2015/925414.
D. C. Pye. A clinical method for estimating the modulus of elasticity of the human cornea in vivo. PLOS One 15 (2020), pp. 1–19. doi: 10.1371/journal.pone.0224824
Modelling implicit pre-cues and collision avoidance in a driving simulator: A pilot study
Get PDFIt is well-established that pre-cues, including those observed in an implicit manner, can affect motor skills and reaction times. However, little research currently exists on how pre-cues influence complex motor skills such as driving a car at high speed. This pilot study investigates the effect of implicit pre-cues on collision avoidance under a repeat trial experiment design using a car driving simulator. Seventeen par- ticipants (aged 23.8 ± 4.2 years) were included in this investigation, which consisted of four different one-kilometre driving scenarios. This investigation considers two of the four scenarios. Two scenarios had the stimulus of a child crossing the road, however only one of these scenarios had an implicit pre-cue appear before the stimulus. The remaining two scenarios had no stimulus or pre-cue and were included to reduce any learning effect by participants. The proportion of participants who had a collision differed significantly between scenarios with and without a pre-cue. The primary effect size of the pre-cue is modelled using a logis- tic regression and distributions for point estimators are obtained from bootstrapping results. A power analysis exploring different primary effect sizes is performed to inform sample size considerations for repeat studies. Implications for motor control, such as experiment design and statistical modelling methods, are discussed to inform future large scale trials.
References
J. A. Barela, A. A. Rocha, A. R. Novak, J. Fransen, and G. A. Figueiredo. Age differences in the use of implicit visual cues in a response time task. Braz. J. Motor Behav. 13.2 (2019), pp. 86–93. doi: 10.20338/bjmb.v13i2.139
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U. Eversheim and O. Bock. The role of precues in the preparation of motor responses in humans. J. Mot. Behav. 34.3 (2002), pp. 271–276. doi: 10.1080/00222890209601945
D. G. Jenkins and P. F. Quintana-Ascencio. A solution to minimum sample size for regressions. PLOS One 15.2 (2020), e0229345. doi: 10.1371/journal.pone.0229345
J. Jiang. Linear and generalized linear mixed models and their applications. Springer Series in Statistics. Springer, 2007. doi: 10.1007/978-0-387-47946-0
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H. C. Kraemer, J. Mintz, A. Noda, J. Tinklenberg, and J. A. Yesavage. Caution regarding the use of pilot studies to guide power calculations for study proposals. Arch. Gen. Psych. 63.5 (2006), pp. 484–489. doi: 10.1001/archpsyc.63.5.484
J. A. Nelder and R. W. M. Wedderburn. Generalized linear models. J. Roy. Stat. Soc. 135.3 (1972), pp. 370–384. doi: 10.2307/2344614
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On an integral of -Bessel functions and its application to Mahler neasure
No full texthttp://dx.doi.org/10.1017/S000497271200033
Intersection of conjugate solvable subgroups in finite classical groups
No full texthttp://dx.doi.org/10.1017/S000497271200033
Confidence intervals in general regression models that utilise uncertain prior information
No full texthttp://dx.doi.org/10.1017/S000497271200033
