Influence of adsorbate interaction on transport in confined spaces

This article provides a review of the recent theory of transport in nanopores developed in the author's laboratory. In particular the influence of fluid-solid interactions on the transport coefficient is examined, showing that such interactions reduce the value of the coefficient by almost an order of magnitude in comparison to the Knudsen theory for non-interacting systems. The activation energy and potential energy barriers for diffusion in smooth pores with a one-dimensional potential energy profile are also discussed, indicating the inadequacy of the commonly used assumption of proportionality between the activation energy and heat of adsorption or the minimum pore potential energy. A further feature affected by fluid-solid interactions is the nature of the reflection of fluid molecules colliding with a pore wall surface, varying from being nearly specular - such as in carbon nanotubes - to nearly diffuse for amorphous solids. Diffuse reflection leads to momentum loss and reduced transport coefficients. However, fluid-solid interactions do not affect the transport coefficient in the single-file diffusion regime when the surface reflection is diffuse, and the transport coefficient in this case is largely independent of the adsorbed density.

Neighborhood environmental factors correlated with walking near home: Using SPACES

Purpose: The physical environment plays an important role in influencing participation in physical activity, although the specific factors that are correlated with different patterns of walking remain to be determined We examined correlations between physical environmental factors and self-reported walking for recreation and transport near home. Methods: The local neighborhood environments (defined as a 400-m radius from the respondent's home) of 1678 adults were assessed for their suitability for walking. The environmental data were collected during 2000 using the Systematic Pedestrian and Cycling Environmental Scan (SPACES) instrument together with information from other sources. We used logistic regression modeling to examine the relationship between the attributes of the physical environment and the self-reported walking behavior undertaken near home. Results: Functional features were correlated with both walking for recreation (odds ratio (OR) 1.62; 95% confidence interval (Cl): 1.20-2.19) and for transport (OR 1.30; 95% Cl: 0.97-1.73). A well-maintained walking surface was the main functional factor associated with walking for recreation (OR 2.04; 95% Cl: 1.43-2.91) and for transport (OR 2.13; 95% Cl: 1.53-2.96). Destination factors, such as shops and public transport, were significantly correlated with walking for transport (OR 1.80; 95% Cl: 1.33-2.44), but not recreation. Conclusion: The findings suggest that neighborhoods with pedestrian facilities that are attractive and comfortable and where there are local destinations (such as shops and public transport) are associated with walking near home.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification

Racing algorithms have recently been proposed as a general-purpose method for performing model selection in machine teaming algorithms. In this paper, we present an empirical study of the Hoeffding racing algorithm for selecting the k parameter in a simple k-nearest neighbor classifier. Fifteen widely-used classification datasets from UCI are used and experiments conducted across different confidence levels for racing. The results reveal a significant amount of sensitivity of the k-nn classifier to its model parameter value. The Hoeffding racing algorithm also varies widely in its performance, in terms of the computational savings gained over an exhaustive evaluation. While in some cases the savings gained are quite small, the racing algorithm proved to be highly robust to the possibility of erroneously eliminating the optimal models. All results were strongly dependent on the datasets used.

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.