Determination of Mass Transfer Parameters by Means of Chemical Absorption

The paper presents an explicit equation for the enhancement factor of a fast irreversible second‐order reaction. The equation makes it possible to determine the effective interfacial area and the liquid‐phase mass‐transfer coefficient of this reaction regime. With the help of a new plot as described in this paper, the Danckwerts plot, and the method for determining the interfacial area by means of a pseudo‐first‐order reaction the paper discusses a novel method for characterizing the reaction regime of experimental data.     

Responses of Sugar Beet (Beta vulgaris) to Foliar Sprays of Copper

Previous studies have shown positive yield responses from foliar copper (Cu) applications to beet. However, some of these responses may have been due to fungicidal instead of nutritional effects. Also, since these studies, national yields have increased possibly increasing the Cu requirement of the beet crop. So, between 1987 and 1990 six field experiments tested the effect of foliar sprays of copper on the yield and processing quality of sugar beet. The copper was applied as Cu oxychloride or an inorganic Cu formulation, in factorial combination with a wetter or an adjuvant. Four of the experiments were on loamy sand textured soils, the remainder on peaty soils. The Cu sprays did not affect beet yields for crops grown on the peat soils. On two of the light textured mineral soils the Cu sprays did increase yield, but there was no clear relationship between soil ethylene diamine tetra acetic acid (EDTA) extractable Cu and yield response to added Cu. There was little difference between the forms of Cu tested on yield, or between the wetter or the adjuvant. Cu sprays did not affect beet processing quality, even when large yield responses were obtained. An analysis of the geographical distribution of sugar beet quota and soil extractable Cu showed that 15% of the UK's national crop could be affected by Cu deficiency.     

Stability of microstructures in chill-cast aluminum alloys containing twinned columnar growth structures

Most chill-cast commercial aluminum alloys, when inappropriately ingot grain refined, will contain twinned columnar growth (TCG) structures near the chill surface of the ingot. These structures consist of large grains composed of parallel arrays of continuous thin lamellae. Each lamella contains a central {111} coherent twin boundary and a noncoherent rough boundary at the edges. This paper describes the stability of such grain structures as affected by composition and postcasting processes such as ingot preheating and deformation. A series of simple binary and two complex high-strength aluminum alloys were investigated. In single-phase alloys, such as preheated binary alloys with compositions less than the maximum solid solubility, the TCG structure was eliminated by a grain growth process. In dual- and multiphase alloys, the stability of the structure depends on the size and distribution of particles after preheating and the nature of the particle/matrix interface. The interplay between particle stability, size, and distribution controls the stability of the TCG-containing microstructure during postcasting processing.     

The hybrid representation model for web document classification

Most Web content categorization methods are based on the vector space model of information retrieval. One of the most important advantages of this representation model is that it can be used by both instance‐based and model‐based classifiers. However, this popular method of document representation does not capture important structural information, such as the order and proximity of word occurrence or the location of a word within the document. It also makes no use of the markup information that can easily be extracted from the Web document HTML tags. A recently developed graph‐based Web document representation model can preserve Web document structural information. It was shown to outperform the traditional vector representation using the k‐Nearest Neighbor (k‐NN) classification algorithm. The problem, however, is that the eager (model‐based) classifiers cannot work with this representation directly. In this article, three new hybrid approaches to Web document classification are presented, built upon both graph and vector space representations, thus preserving the benefits and overcoming the limitations of each. The hybrid methods presented here are compared to vector‐based models using the C4.5 decision tree and the probabilistic Naïve Bayes classifiers on several benchmark Web document collections. The results demonstrate that the hybrid methods presented in this article outperform, in most cases, existing approaches in terms of classification accuracy, and in addition, achieve a significant reduction in the classification time. © 2008 Wiley Periodicals, Inc.     

A compact and accurate model for classification

We describe and evaluate an information-theoretic algorithm for data-driven induction of classification models based on a minimal subset of available features. The relationship between input (predictive) features and the target (classification) attribute is modeled by a tree-like structure termed an information network (IN). Unlike other decision-tree models, the information network uses the same input attribute across the nodes of a given layer (level). The input attributes are selected incrementally by the algorithm to maximize a global decrease in the conditional entropy of the target attribute. We are using the prepruning approach: when no attribute causes a statistically significant decrease in the entropy, the network construction is stopped. The algorithm is shown empirically to produce much more compact models than other methods of decision-tree learning while preserving nearly the same level of classification accuracy.