A posteriori error estimates of discontinuous Galerkin methods for the Signorini problem

A reliable and efficient a posteriori error estimator is derived for a class of discontinuous Galerkin (DG) methods for the Signorini problem. A common property shared by many DG methods leads to a unified error analysis with the help of a constraint preserving enriching map. The error estimator of DG methods is comparable with the error estimator of the conforming methods. Numerical experiments illustrate the performance of the error estimator. (C) 2015 Elsevier B.V. All rights reserved.

Bearing capacity of a circular foundation on layered sand-clay media

The bearing capacity of a circular footing lying over fully cohesive strata, with an overlaying sand layer, is computed using the axisymmetric lower bound limit analysis with finite elements and linear optimization. The effects of the thickness and the internal friction angle of the sand are examined for different combinations of c(u)/(gamma b) and q, where c(u)=the undrained shear strength of the cohesive strata, gamma=the unit weight of either layer, b=the footing radius, and q=the surcharge pressure. The results are given in the form of a ratio (eta) of the bearing capacity with an overlaying sand layer to that for a footing lying directly over clayey strata. An overlaying medium dense to dense sand layer considerably improves the bearing capacity. The improvement continuously increases with decreases in c(u)/(gamma b) and increases in phi and q/(gamma b). A certain optimum thickness of the sand layer exists beyond which no further improvement occurs. This optimum thickness increases with an increase in 0 and q and with a decrease in c(u)/(gamma b). Failure patterns are also drawn to examine the inclusion of the sand layer. (C) 2015 The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.

Linking monazite geochronology with fluid infiltration and metamorphic histories: Nature and experiment

Migmatised metapelites from the Kodaikanal region, central Madurai Block, southern India have undergone ultrahigh-temperature metamorphism (950-1000 degrees C; 7-8 kbar). In-situ electron microprobe Th-U-Pb isochron (CHIME) dating of monazites in a leucosome and surrounding silica-saturated and silica-poor restites from the same outcrop indicates three principal ages that can be linked to the evolutionary history of these rocks. Monazite grains from the silica-saturated restite have well-defined, inherited cores with thick rims that yield an age of ca. 1684 Ma. This either dates the metamorphism of the original metapelite or is a detrital age of inherited monazite. Monazite grains from the silica-poor restite, thick rims from the silica-saturated restite, and monazite cores from the leucosome have ages ranging from 520 to 540 Ma suggesting a mean age of 530 Ma within the error bars. In the leucosome the altered rim of the monazite gives an age of ca. 502 Ma. Alteration takes the form of Th-depleted lobes of monazite with sharp curvilinear boundaries extending from the monazite grain rim into the core. We have replicated experimentally these altered rims in a monazite-leucosome experiment at 800 degrees C and 2 kbar. This experiment, coupled with earlier published monazite-fluid experiments involving high pH alkali-bearing fluids at high P-T, helps to confirm the idea that alkali-bearing fluids, in the melt and along grain boundaries during crystallization, were responsible for the formation of the altered monazite grain rims via the process of coupled dissolution-reprecipitation. (C) 2015 Elsevier B.V. All rights reserved.

Spectral solutions to the Korteweg-de-Vries and nonlinear Schrodinger equations

In this paper, we present the solutions of 1-D and 2-D non-linear partial differential equations with initial conditions. We approach the solutions in time domain using two methods. We first solve the equations using Fourier spectral approximation in the spatial domain and secondly we compare the results with the approximation in the spatial domain using orthogonal functions such as Legendre or Chebyshev polynomials as their basis functions. The advantages and the applicability of the two different methods for different types of problems are brought out by considering 1-D and 2-D nonlinear partial differential equations namely the Korteweg-de-Vries and nonlinear Schrodinger equation with different potential function. (C) 2015 Elsevier Ltd. All rights reserved.

Performance evaluation of mobile agent based resource management protocol for MANETs

The nodes with dynamicity, and management without administrator are key features of mobile ad hoc networks (1VIANETs). Increasing resource requirements of nodes running different applications, scarcity of resources, and node mobility in MANETs are the important issues to be considered in allocation of resources. Moreover, management of limited resources for optimal allocation is a crucial task. In our proposed work we discuss a design of resource allocation protocol and its performance evaluation. The proposed protocol uses both static and mobile agents. The protocol does the distribution and parallelization of message propagation (mobile agent with information) in an efficient way to achieve scalability and speed up message delivery to the nodes in the sectors of the zones of a MANET. The protocol functionality has been simulated using Java Agent Development Environment (JADE) Framework for agent generation, migration and communication. A mobile agent migrates from central resource rich node with message and navigate autonomously in the zone of network until the boundary node. With the performance evaluation, it has been concluded that the proposed protocol consumes much less time to allocate the required resources to the nodes under requirement, utilize less network resources and increase the network scalability. (C) 2015 Elsevier B.V. All rights reserved.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.