Abstract
One of the classical problems in the structural optimization field is to find the stiffest truss, under a given load and with a bound on the total volume. This is a well-studied problem for continuous cross sectional areas. Generally, the optimal solutions obtained for this problem contain bars with many different cross sectional areas. However, in real life, only a finite set of possible values for those cross sectional areas can be considered. We propose a Semidefinite Programming with discrete variables for this problem. In order to solve the problem we derive and compare two exact algorithms. The first one is a branch and bound algorithm where the branching is done only on the bar-areas. The second algorithm has two stages. In the first stage a branch and bound on the nodes of the structure is performed. In the second stage, considering the nodes in the structure from the first stage, a branch and bound algorithm on the bar-areas is performed.

Abstract
In this paper we examine prior research, which has been studying the effects of switching costs on customer retention, within online and offline markets. The empirical studies in the area reveal contrasting results: (1) switching costs are a stronger predictor of customer retention than customer satisfaction alone; (2) the main effect of switching costs moderates the relationship between satisfaction and customer retention; (3) conversely, the lack of main effects reflects the interaction effects indicating that switching costs have an even greater impact on customer retention than under satisfaction variation levels. These power asymmetries reveal a conflicting role of switching costs across industry and marketplaces. We also observe a significant bias in favor of service industries with high switching costs. Based on this meta-analytical research we propose a theoretical base to support the path structural relationship between the constructs providing a nomological framework to future research. Finally we draw some conclusions, suggest future research directions and make managerial recommendations. © 2009 IADIS.

Abstract

Abstract
Solvent extraction is considered as a multi-criteria optimization problem, since several chemical species with similar extraction kinetic properties are frequently present in the aqueous phase and the selective extraction is not practicable. This optimization, applied to mixer-settler units, considers the best parameters and operating conditions, as well as the best structure or process flow-sheet. Global process optimization is performed for a specific flow-sheet and a comparison of Pareto curves for different flow-sheets is made. The positive weight sum approach linked to the sequential quadratic programming method is used to obtain the Pareto set. In all investigated structures, recovery increases with hold-up, residence time and agitation speed, while the purity has an opposite behaviour. For the same treatment capacity, counter-current arrangements are shown to promote recovery without significant impairment in purity. Recycling the aqueous phase is shown to be irrelevant, but organic recycling with as many stages as economically feasible clearly improves the design criteria and reduces the most efficient organic flow-rate.

Abstract
This paper presents the implementation of an algorithm for automatic identification of drops with different sizes in monochromatic digitized frames of a liquid-liquid chemical process. These image frames were obtained at our Laboratory, using a nonintrusive process, with a digital video camera, a microscope, and an illumination setup from a dispersion of toluene in water within a transparent mixing vessel. In this implementation, we propose a two-phase approach, using a Hough transform that automatically identifies drops in images of the chemical process. This work is a promising starting point for the possibility of performing an automatic drop classification with good results. Our algorithm for the analysis and interpretation of digitized images will be used for the calculation of particle size and shape distributions for modelling liquid-liquid systems.

Abstract
Molecular dynamics simulations is a valuable tool to study protein unfolding in silico. Analyzing the relative spatial position of the residues during the simulation may indicate which residues are essential in determining the protein structure. We present a method, inspired by a popular data mining technique called Frequent Itemset Mining, that clusters sets of amino acid residues with a synchronized trajectory during the unfolding process. The proposed approach has several advantages over traditional hierarchical clustering. © 2009 Springer Berlin Heidelberg.