Exact formulae for the electrical resistivity

The theory of exact formulae for the electrical resistivity is reviewed. The derivation of an exact formula for the resistivity and the proof of the equivalence of this formula with the linear response formula for the electrical conductivity are presented. One form of the resistivity formula is evaluated in the limit of weak scattering. The model for this calculation is a system of non-interacting free electrons scattered by random fixed impurities. It is shown that the evidence which suggested that the resistivity formula is simpler to evaluate than the conductivity formula is misleading.     

A phase I, randomized study of combined IL-2 and therapeutic immunisation with antiretroviral therapy

Background  

Fully functional HIV-1-specific CD8 and CD4 effector T-cell responses are vital to the containment of viral activity and disease progression. These responses are lacking in HIV-1-infected patients with progressive disease. We attempted to augment fully functional HIV-1-specific CD8 and CD4 effector T-cell responses in patients with advanced chronic HIV-1 infection.     

Development of a micromachined piezoelectric microphone for aeroacoustics applications

This paper describes the design, fabrication, and characterization of a bulk-micromachined piezoelectric microphone for aeroacoustic applications. Microphone design was accomplished through a combination of piezoelectric composite plate theory and lumped element modeling. The device consists of a 1.80-mm-diam, 3-microm-thick, silicon diaphragm with a 267-nm-thick ring of piezoelectric material placed near the boundary of the diaphragm to maximize sensitivity. The microphone was fabricated by combining a sol-gel lead zirconate-titanate deposition process on a silicon-on-insulator wafer with deep-reactive ion etching for the diaphragm release. Experimental characterization indicates a sensitivity of 1.66 microVPa, dynamic range greater than six orders of magnitude (35.7-169 dB, re 20 microPa), a capacitance of 10.8 nF, and a resonant frequency of 59.0 kHz.     

Colour in visualisation for computational fluid dynamics

Colour is used in computational fluid dynamic (CFD) simulations in two key ways. First it is used to visualise the geometry and allow the engineer to be confident that the model constructed is a good representation of the engineering situation. Once an analysis has been completed, colour is used in post-processing the data from the simulations to illustrate the complex fluid mechanic phenomena under investigation. This paper describes these two uses of colour and provides some examples to illustrate the key visualisation approaches used in CFD.     

A linear algorithm of the reference region model for DCE-MRI is robust and relaxes requirements for temporal resolution

Dynamic contrast enhanced MRI (DCE-MRI) has utility for improving clinical diagnoses of solid tumors, and for evaluating the early responses of anti-angiogenic chemotherapies. The Reference Region Model (RRM) can improve the clinical implementation of DCE-MRI by substituting the contrast enhancement of muscle for the Arterial Input Function that is used in traditional DCE-MRI methodologies. The RRM is typically fitted to experimental results with a non-linear least squares algorithm. This report demonstrates that this algorithm produces inaccurate and imprecise results when DCE-MRI results have low SNR or slow temporal resolution. To overcome this limitation, a linear least-squares algorithm has been derived for the Reference Region Model. This new algorithm improves accuracy and precision of fitting the Reference Region Model to DCE-MRI results, especially for voxel-wise analyses. This linear algorithm is insensitive to injection speeds, and has 300- to 8000-fold faster calculation speed relative to the non-linear algorithm. The linear algorithm produces more accurate results for over a wider range of permeabilities and blood volumes of tumor vasculature. This new algorithm, termed the Linear Reference Region Model, has strong potential to improve clinical DCE-MRI evaluations.     

Calibration of the Pseudo-Reaction-Zone model for detonation wave propagation

An approach for the calibration of an advanced programmed burn (PB) model for detonation performance calculations in high explosive systems is detailed. Programmed burn methods split the detonation performance calculation into two components: timing and energy release. For the timing, the PB model uses a Detonation Shock Dynamics (DSD) surface propagation model, where the normal surface speed is a function of local surface curvature. For the energy release calculation and subsequent hydrodynamic flow evolution, a Pseudo-Reaction-Zone (PRZ) model is used. The PRZ model is similar to a reactive burn model in that it converts reactants into products at a finite rate, but it has a reaction rate dependent on the normal surface speed derived from the DSD calculation. The PRZ reaction rate parameters must be calibrated in such a way that the rate of energy release due to reaction in multi-dimensional geometries is consistent with the timing calculation provided by the DSD model. Our strategy for achieving this is to run the PRZ model in a detonation shock-attached frame in a compliant 2D planar slab geometry in an equivalent way to a reactive burn model, from which we can generate detonation front shapes and detonation phase speed variations with slab thickness. In this case, the D _n field used by the PRZ model is then simply the normal detonation shock speed rather than the DSD surface normal speed. The PRZ rate parameters are then iterated on to match the equivalent surface front shapes and surface phase speed variations with slab thickness derived from the target DSD model. For the purposes of this paper, the target DSD model is fitted to the performance properties of an idealised condensed-phase reactive burn model, which allows us to compare the detonation structure of the calibrated PRZ model to that of the originating idealised-condensed phase model.     

Random graph models for dynamic networks

Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.