Goal-oriented a posteriori error estimates for transport problems

Some aspects of goal-oriented a posteriori error estimation are addressed in the context of steady convection–diffusion equations. The difference between the exact and approximate values of a linear target functional is expressed in terms of integrals that depend on the solutions to the primal and dual problems. Gradient averaging techniques are employed to separate the element residual and diffusive flux errors without introducing jump terms. The dual solution is computed numerically and interpolated using higher-order basis functions. A node-based approach to localization of global errors in the quantities of interest is pursued. A possible violation of Galerkin orthogonality is taken into account. Numerical experiments are performed for centered and upwind-biased approximations of a 1D boundary value problem.     

Performance models for wireless channels

Performance modeling of wireless access technologies is useful to understand their limitations in various operational conditions and find a way to improve their performance. In the past two decades a number of models have been proposed. These models are often more complicated compared to those developed for wired networks. The reason is that in wireless networks performance degradation is caused by both incorrect reception of channel symbols at the physical layer and queuing at higher layers. Various error control mechanisms used to hide the effect of error-prone channel behavior complicate performance analysis and often require restrictive assumptions to retain analytical tractability. The aim of this paper is to review performance evaluation models proposed for wireless channels, highlighting their basic ideas, shortcomings, and advantages. We consider models developed for both centralized and distributed access technologies. Potential applications and extensions are also discussed. We believe that this study may provide a starting point for those looking for a suitable modeling framework and allow time to be saved in developing new performance models.     

Direct numerical inversion method for kinetic ellipsometric data. I. Presentation of the method and numerical evaluation

A direct numerical inversion method for the determination of the refractive index and the thickness of the outermost layer of a thin transparent film on top of a multilayer has been developed. This method is based on a second-order Taylor decomposition of the coefficients of the Abelès matrices of the newly grown layer. The variations of the real-time spectroscopic ellipsometry data are expressed as polynomial fuctions depending on the dielectric constant and the thickness of the newly grown layer. The method is fast, capable of single-wavelength and multiwavelength inversion of continuous as well as discontinuous-index profiles, and can be adapted to many different polarimetric instruments.     

Pattern storage and processing in attractor networks with short-time synaptic dynamics

Neurophysiological experiments show that the strength of synaptic connections can undergo substantial changes on a short time scale. These changes depend on the history of the presynaptic input. Using mean-field techniques, we study how short-time dynamics of synaptic connections influence the performance of attractor neural networks in terms of their memory capacity and capability to process external signals. For binary discrete-time as well as for firing rate continuous-time neural networks, the fixed points of the network dynamics are shown to be unaffected by synaptic dynamics. However, the stability of patterns changes considerably. Synaptic depression turns out to reduce the storage capacity. On the other hand, synaptic depression is shown to be advantageous for processing of pattern sequences. The analytical results on stability, size of the basins of attraction and on the switching between patterns are complemented by numerical simulations.     

Rational design and selection of bivalent peptide ligands of thrombin incorporating P4-P1 tetrapeptide sequences: from good substrates to potent inhibitors

The tetrapeptide Phe-Asn-Pro-Arg is a structurally optimized sequence for binding to the active site of thrombin. By conjugating this tetrapeptide or some variants to a C-terminal fragment of hirudin, we were able to generate a series of new bivalent inhibitors of thrombin containing only genetically encodable natural amino acids. We found that synergistic binding to both the active site and an exosite of thrombin can be enhanced through substitutions of amino acid residues at the P3 and P3' sites of the active-site directed sequence, Phe(P4)-Xaa(P3)-Pro(P2)-Arg(P1)-Pro(P1')-Gln(P2')-Yaa(P3'). Complementary to rational design, a phage library was constructed to explore further the residue requirements at the P4, P3 and P3' sites for bivalent and optimized two-site binding. Very significantly, panning of the phage library has led to thrombin-inhibitory peptides possessing strong anti-clotting activities in the low nanomolar range and yet interfering only partially the catalytic active site of thrombin. Modes of action of the newly discovered bivalent inhibitors are rationalized in light of the allosteric properties of thrombin, especially the interplay between the proteolytic action and regulatory binding occurring at thrombin surfaces remote from the catalytic active site.     

Syntheses and properties of B-C-N and BN nanostructures

The current status of research on boron-carbon-nitrogen (B-C-N) and boron nitride (BN) nanotubes is presented. The latest achievements in syntheses, analyses and property measurements of these nanoscale tubular architectures are reviewed. The characteristic features of B-C-N and BN nanotubes, compared with conventional C nanotubes, are paid special attention. In particular, the latest breakthroughs in the chemical vapour deposition synthesis of BN nanotubes and an insight into their unique structures are highlighted. A wide range of potential applications is also envisaged, based on the recent progress, which includes pioneering results in BN nanocable fabrication, gas adsorption, electron transport and field emission measurements.     

Fast similarity join for multi-dimensional data

The efficient processing of multidimensional similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focused on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memory available on current computers, and the need for efficient processing of spatial joins suggest that spatial joins for a large class of problems can be processed in main memory. In this paper, we develop two new in-memory spatial join algorithms, the Grid-join and EGO*-join, and study their performance. Through evaluation, we explore the domain of applicability of each approach and provide recommendations for the choice of a join algorithm depending upon the dimensionality of the data as well as the expected selectivity of the join. We show that the two new proposed join techniques substantially outperform the state-of-the-art join algorithm, the EGO-join.