Immersed Boundary Method for generalised finite volume and finite difference Navier-Stokes solvers

In Immersed Boundary Methods (IBM) the effect of complex geometries is introduced through the forces added in the Navier-Stokes solver at the grid points in the vicinity of the immersed boundaries. Most of the methods in the literature have been used with Cartesian grids. Moreover many of the methods developed in the literature do not satisfy some basic conservation properties (the conservation of torque, for instance) on non-uniform meshes. In this paper we will follow the RKPM method originated by Liu et al. [1] to build locally regularized functions that verify a number of integral conditions. These local approximants will be used both for interpolating the velocity field and for spreading the singular force field in the framework of a pressure correction scheme for the incompressible Navier-Stokes equations. We will also demonstrate the robustness and effectiveness of the scheme through various examples. Copyright © 2010 by ASME.

Subdivision-stabilised immersed b-spline finite elements for moving boundary flows

An immersed finite element method is presented to compute flows with complex moving boundaries on a fixed Cartesian grid. The viscous, incompressible fluid flow equations are discretized with b-spline basis functions. The two-scale relation for b-splines is used to implement an elegant and efficient technique to satisfy the LBB condition. On non-grid-aligned fluid domains and at moving boundaries, the boundary conditions are enforced with a consistent penalty method as originally proposed by Nitsche. In addition, a special extrapolation technique is employed to prevent the loss of numerical stability in presence of arbitrarily small cut-cells. The versatility and accuracy of the proposed approach is demonstrated by means of convergence studies and comparisons with previous experimental and computational investigations.

Optimized vertical carbon nanotube forests for multiplex surface-enhanced raman scattering detection

The highly sensitive and molecule-specific technique of surface-enhanced Raman spectroscopy (SERS) generates high signal enhancements via localized optical fields on nanoscale metallic materials, which can be tuned by manipulation of the surface roughness and architecture on the submicrometer level. We investigate gold-functionalized vertically aligned carbon nanotube forests (VACNTs) as low-cost straightforward SERS nanoplatforms. We find that their SERS enhancements depend on their diameter and density, which are systematically optimized for their performance. Modeling of the VACNT-based SERS substrates confirms consistent dependence on structural parameters as observed experimentally. The created nanostructures span over large substrate areas, are readily configurable, and yield uniform and reproducible SERS enhancement factors. Further fabricated micropatterned VACNTs platforms are shown to deliver multiplexed SERS detection. The unique properties of CNTs, which can be synergistically utilized in VACNT-based substrates and patterned arrays, can thus provide new generation platforms for SERS detection. © 2012 American Chemical Society.

Multidirectional scattering models for 3-dimensional ultrasound imaging

An ultrasound image is created from backscattered echoes originating from both diffuse and directional scattering. It is potentially useful to separate these two components for the purpose of tissue characterization. This article presents several models for visualization of scattering fields on 3-dimensional (3D) ultrasound imaging. By scanning the same anatomy from multiple directions, we can observe the variation of specular intensity as a function of the viewing angle. This article considers two models for estimating the diffuse and specular components of the backscattered intensity: a modification of the well-known Phong reflection model and an existing exponential model. We examine 2-dimensional implementations and also propose novel 3D extensions of these models in which the probe is not constrained to rotate within a plane. Both simulation and experimental results show that improved performance can be achieved with 3D models. © 2013 by the American Institute of Ultrasound in Medicine.

Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.