Solving Multi-Objective Hub Location Problems with Robustness

Hub location problems (HLP) are considered in many logistic, telecommunications, and computer problems, where the design of these networks are optimized based on some objective(s) related to the cost or service. In those cases, direct routing between any origin and destination is not viable due to economic or technological constraints. From the seminal work of O'Kelly~\cite{OKelly86}, a huge number of works have been published in the literature. Early contributions were focused on analogue facility location problems, considering some assumptions to simplify the network design. Recent works have studied more complex models by incorporating additional real-life features and relaxing some assumptions, although the input parameters are still assumed to be known in most of the HLPs considered in the literature. This assumption is unrealistic in practice, since there is a high uncertainty on relevant parameters of real problems, such as costs, demands, or even distances. Consequently, a decision maker usually prefer several solutions with a low uncertainty in their objectives functions instead of the optimum solution of an assumed deterministic objective function. In this work we use a three-objective Integer Linear Programming model of the p-hub location problem where the average transportation cost, its variance, and the processing time in the hubs are minimized. The number of variables is $O(n^4)$ where $n$ is the number of nodes of the graph. ILP solvers can only solve small instances of the problems and we propose in this work the use of a recent hybrid algorithm combining a heuristic and exact methods: Construct, Merge, Solve, and AdaptUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Anytime Algorithms for Multi-Objective Hub Location Problems

In many logistic, telecommunications and computer networks, direct routing of commodities between any origin and destination is not viable due to economic and technological constraints. Hub locations problems (HLPs) are considered in that cases, where the design of these networks are optimized based on some objective(s) related on the cost or service. A huge number of papers have been published since the seminal work of O’Kelly. Early works were focused on analogue facility location problems, considering some assumptions to simplify network design. Recent works have studied more complex models that relax some of these assumptions and incorporate additional real-life features. In most HLPs considered in the literature, the input parameters are assumed to be known and deterministic. However, in practice, this assumption is unrealistic since there is a high uncertainty on relevant parameters, such as costs, demands or even distances. As a result, a decision maker usually prefer several solutions with a low uncertainty in their objectives functions. In this work, anytime algorithms are proposed to solve the multi-objective hub location problems with uncertainty. The proposed algorithms can be stopped at any time, yielding a set of efficient solutions (belonging to the Pareto front) that are well spread in the objective space.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Decision Tree and S-Transform Based Approach for Power Quality Disturbances Classification

In this paper, it is presented an automated classification based on S-transform as feature extraction tool and Decision Tree as algorithm classifier. The signals generated according to mathematical models, including complex disturbances, have been used to design and test this approach, where noise is added to the signals from 40dB to 20dB. Finally, several disturbances, simple and complex, have been considered to test the implemented system. Evaluation results verifying the accuracy of the proposed method are presented.IEE

A DSP-BASED active contour model

In this paper a DSP-based active contour model for tracking of the endocardium in a sequence of echocardiographic images is presented. If a contour is available in the first frame of a sequence, the contours in the subsequent frames are segmented. Deformable active contours is a technique that combine geometry, physics and approximation theory in order to solve problems of fundamental importance to medical image analysis; such as segmentation, representation and matching of shapes, and the tracking of objects in movement. The procedure has been developed on a DSP processor using its hardware features. The results are illustrated using a sequence of four-chambers apical echocardiographic images

Reduction of the speckle noise in echocardiographic images by a cubic spline filter

One of the main problems to resolve in the processing of biomedical images is the reduction of noise. The problem is specially important if the noise has a multiplicative nature (speckle noise), for instance if the object of analysis is an ultrasonic image. In this report we carry out a review of techniques which can be used to reduce this type of noise on four-chamber view B-mode echocardiographic images in an appropriated way. Different ways of nonlinear filtering, adaptive techniques based on the statistical ordering and a cubic spline interpolation will be shown as suitable techniques for this objective but regarding quantitative and qualitative results we have obtained, we can confirm that a cubic spline filter is the most suitable filter that we have reviewed.This work has been supported by Fundación Séneca of Región de Murcia and Ministerio de Ciencia y Tecnología of Spain, under grants PB/63/FS/02 and TIC2003-09400- C04-02, respectively

Effect of a short-term physical education-based flexibility program on hamstring and lumbar extensibility and its posterior reduction in primary schoolchildren

The purpose of this study was to examine the effects of a short-term flexibility program on hamstring and lumbar extensibility and its posterior reduction among primary schoolchildren in a physical education (PE) setting. Forty-five 10-to-11-year-old schoolchildren from two classes were clustered randomly to an experimental group (EG) (n=22) or a control group (CG) (n=23). During the PE classes, the students in EG performed a six-minute flexibility program twice a week for eight weeks. Subsequently, these students underwent a five-week detraining period. The results of the two-way ANOVA showed that the intervention program significantly increased the students’ hamstring and lumbar extensibility (pretest=15.7±7.0 cm; posttest=18.2±7.7 cm; p<.001). Although after the detraining period flexibility levels decreased statistically significantly (retest=17.1±7.9 cm; p<.001), the students from EG presented statistically higher values than in the baseline flexibility level (p=.006). For the CG no significant differences were found (pretest=13.4±8.5 cm; posttest=13.1±8.5 cm; retest=13.2±8.4 cm; p=1.000). Although children lose a significant part of the obtained flexibility gains over a five-week detraining period, they do not revert to their baseline flexibility level. Hence, the students might continue working on their flexibility within the next five weeks in order to maintain the gains obtained previously. These findings could help teachers to design programs that guarantee feasible improvement and maintenance of children’s flexibility in a physical education setting

An automatic welding defects classifier system

Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases

Cotton fertilization with compost of (sugarbeet) vinasse and agricultural residues

A concentrated depotassified beet vinasse was mixed with each of ten solid agricultural residues. The ten mixtures were composted for 7 months. The composts obtained after this period were used to fertilize a cotton crop. A mineral treatment was used for comparison and a treatment without fertilization was used as control. The nitrate content of petiole determined before the first top dressing revealed significant differences between treatments. All treatments produced higher yields than the control. Analysis of fibre quality did not show significant differences between treatments

Modulation of autoimmune arthritis severity in mice by Apolipoprotein E (ApoE) and cholesterol

Apolipoprotein E (ApoE) deficiency promoted an exacerbation of autoimmune arthritis in mice by inducing pro-inflammatory immune responses. In this study we analysed the contribution of hypercholesterolemia and/or the absence of ApoE anti-inflammatory properties, unrelated to its function in the control of cholesterol metabolism, towards the acceleration of arthritis in these mutant animals. The induction and severity of collagen type II-induced arthritis (CIA) were compared for B10.RIII wild type (WT), B10.RIII.ApoE+/- , B10.RIII.ApoE-/- and B10.RIII.LDLR-/- mice with different concentrations of circulating ApoE and cholesterol. A 50-70% reduction in serum levels of ApoE was observed in heterozygous B10.RIII.ApoE+/- mice in comparison to B10.RIII.WT, although both strains of mice exhibited similar circulating lipid profiles. This ApoE reduction was associated with an increased CIA severity that remained lower than in homozygous B10.RIII.ApoE-/- mice. An important rise in circulating ApoE concentration was observed in hypercholesterolemic B10.RIII.LDLR-/- mice fed with a normal chow diet, and both parameters further increased with an atherogenic hypercholesterolemic diet. However the severity of CIA in B10.RIII.LDLR-/- mice was similar to that of B10.RIII.WT controls. In conclusion, by comparing the evolution of CIA between several strains of mutant mice with different levels of serum ApoE and cholesterol, our results demonstrate that both hypercholesterolemia and ApoE regulate the intensity of in vivo systemic autoimmune responses. This article is protected by copyright. All rights reserve

Implementación de sistemas fuzzy complejos sobre FPGAs

Las desventajas de las soluciones hardware dedicadas para la implementación de sistemas de inferencia fuzzy cuando se comparan con las estrategias basadas en software son principalmente la falta de flexibilidad y la complicación en el proceso de diseño. En este trabajo se presenta una arquitectura novedosa que permite la síntesis electrónica y la implementación hardware de sistemas expertos basados en conocimiento fuzzy. La definición de la arquitectura se basa en la descripción en forma de red de Petri de la base de reglas complejas, heredando de ella las características de modularidad y escalabilidad. Los componentes de nuestra arquitectura se definen entonces utilizando descripciones VHDL de alto nivel. Por ello, nuestra metodología de diseño proporciona flexibilidad, reusabilidad e independencia tanto de la tecnología electrónica como del tipo y tamaño de la aplicación, solucionando la mayoría de las limitaciones del hardware fuzzy