Fundamental dynamics of flow through carbon nanotube membranes

The flow of a model non-polar liquid through small carbon nanotubes is studied using non-equilibrium molecular dynamics simulation. We explain how a membrane of small-diameter nanotubes can transport this liquid faster than a membrane consisting of larger-diameter nanotubes. This effect is shown to be back-pressure dependent, and the reasons for this are explored. The flow through the very smallest nanotubes is shown to depend strongly on the depth of the potential inside, suggesting atomic separation can be based on carbon interaction strength as well as physical size. Finally, we demonstrate how increasing the back-pressure can counter-intuitively result in lower exit velocities from a nanotube. Such studies are crucial for optimisation of nanotube membranes.     

An improved approximation to the precision of fixed effects from restricted maximum likelihood

An approximate small sample variance estimator for fixed effects from the multivariate normal linear model, together with appropriate inference tools based on a scaled F pivot, is now well established in practice and there is a growing literature on its properties in a variety of settings. Although effective under linear covariance structures, there are examples of nonlinear structures for which it does not perform as well. The cause of this problem is shown to be a missing term in the underlying Taylor series expansion which accommodates the bias in the estimators of the parameters of the covariance structure. The form of this missing term is derived, and then used to adjust the small sample variance estimator. The behaviour of the resulting estimator is explored in terms of invariance under transformation of the covariance parameters and also using a simulation study. It is seen to perform successfully in the way predicted from its derivation.     

Genetic adaptive state estimation

A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. This paper describes a GA that can perform on-line adaptive state estimation for linear and nonlinear systems. First, it shows how to construct a genetic adaptive state estimator where a GA evolves the model in a state estimator in real time so that the state estimation error is driven to zero. Next, several examples are used to illustrate the operation and performance of the genetic adaptive state estimator. Its performance is compared to that of the conventional adaptive Luenberger observer for two linear system examples. Next, a genetic adaptive state estimator is used to predict when surge and stall occur in a nonlinear jet engine. Our main conclusion is that the genetic adaptive state estimator has the potential to offer higher performance estimators for nonlinear systems over current methods.     

A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.     

Evaluation of amplitude-based sorting algorithm to reduce lung tumor blurring in PET images using 4D NCAT phantom

PURPOSE: develop and validate a PET sorting algorithm based on the respiratory amplitude to correct for abnormal respiratory cycles. METHOD AND MATERIALS: using the 4D NCAT phantom model, 3D PET images were simulated in lung and other structures at different times within a respiratory cycle and noise was added. To validate the amplitude binning algorithm, NCAT phantom was used to simulate one case of five different respiratory periods and another case of five respiratory periods alone with five respiratory amplitudes. Comparison was performed for gated and un-gated images and for the new amplitude binning algorithm with the time binning algorithm by calculating the mean number of counts in the ROI (region of interest). RESULTS: an average of 8.87+/-5.10% improvement was reported for total 16 tumors with different tumor sizes and different T/B (tumor to background) ratios using the new sorting algorithm. As both the T/B ratio and tumor size decreases, image degradation due to respiration increases. The greater benefit for smaller diameter tumor and lower T/B ratio indicates a potential improvement in detecting more problematic tumors.     

The relation between color discrimination and color constancy: when is optimal adaptation task dependent?

Color vision supports two distinct visual functions: discrimination and constancy. Discrimination requires that the visual response to distinct objects within a scene be different. Constancy requires that the visual response to any object be the same across scenes. Across changes in scene, adaptation can improve discrimination by optimizing the use of the available response range. Similarly, adaptation can improve constancy by stabilizing the visual response to any fixed object across changes in illumination. Can common mechanisms of adaptation achieve these two goals simultaneously? We develop a theoretical framework for answering this question and present several example calculations. In the examples studied, the answer is largely yes when the change of scene consists of a change in illumination and considerably less so when the change of scene consists of a change in the statistical ensemble of surface reflectances in the environment.