Abstract
One of the classical problems in the structural optimization field is the Truss Topology Design Problem (TTDP) which deals with the selection of optimal configuration for structural systems for applications in mechanical, civil, aerospace engineering, among others. In this paper we consider a TTDP where the goal is to find the stiffest truss, under a given load and with a bound on the total volume. The design variables are the cross-section areas of the truss bars that must be chosen from a given finite set. This results in a large-scale non-convex problem with discrete variables. This problem can be formulated as a Semidefinite Programming Problem (SDP problem) with binary variables. We propose a branch and bound algorithm to solve this problem. In this paper it is considered a binary formulation of the problem, to take advantage of its structure, which admits a Knapsack problem as subproblem. Thus, trying to improve the performance of the Branch and Bound, at each step, some valid inequalities for the Knapsack problem are included.

Abstract
We consider the Weight-constrained Minimum Spanning Tree problem (WMST). The WMST aims at finding a minimum spanning tree such that the overall tree weight does not exceed a specified limit on a graph with costs and weights associated with each edge. We present and compare, from the computational point of view, several formulations for the WMST. From preliminary computational results we propose a model that combines a formulation similar to the well known Miller-Tucker-Zemlin formulation with the cut-set inequalities.

Abstract
In this paper we present an algorithm that identifies circular drops of different sizes in monochromatic digitized frames of a liquid-liquid chemical process. These image frames were obtained at our Laboratory, using a non-intrusive process, with a digital video camera, a microscope, and an illumination setup from a dispersion of toluene in water within a transparent mixing vessel. Here we describe in detail the two-phase approach used for the automatic identification of the drops in images of the chemical process, which employs a Hough transform. Empirical evaluation on an independent set of images shows promising results for the automatic classification of the drops. Copyright © 2010 by IJI (CESER Publications).

Abstract
Meiotic and early-embryonic cell divisions in vertebrates take place in the absence of transcription and rely on the translational regulation of stored maternal messenger RNAs. Most of these mRNAs are regulated by the cytoplasmic-polyadenylation-element-binding protein (CPEB), which mediates translational activation and repression through cytoplasmic changes in their poly(A) tail length. It was unknown whether translational regulation by cytoplasmic polyadenylation and CPEB can also regulate mRNAs at specific points of mitotic cell-cycle divisions. Here we show that CPEB-mediated post-transcriptional regulation by phase-specific changes in poly(A) tail length is required for cell proliferation and specifically for entry into M phase in mitotically dividing cells. This translational control is mediated by two members of the CPEB family of proteins, CPEB1 and CPEB4. We conclude that regulation of poly(A) tail length is not only required to compensate for the lack of transcription in specialized cell divisions but also acts as a general mechanism to control mitosis.

Abstract
We consider integer-valued autoregressive models of order one contaminated with innovational outliers. Assuming that the time points of the outliers are known but their sizes are unknown, we prove that Conditional Least Squares (CLS) estimators of the offspring and innovation means are strongly consistent. In contrast, CLS estimators of the outliers' sizes are not strongly consistent. We also prove that the joint CLS estimator of the offspring and innovation means is asymptotically normal. Conditionally on the values of the process at time points preceding the outliers' occurrences, the joint CLS estimator of the sizes of the outliers is asymptotically normal.

Abstract
In this paper, an accurate method that estimates the HNR from sustained vowels based on harmonic structure modeling is proposed. Basically, the proposed algorithm creates an accurate harmonic structure where each harmonic is parameterized by frequency, magnitude and phase. The harmonic structure is then synthesized and assumed as the harmonic component of the speech signal. The noise component can be estimated by subtracting the harmonic component from the speech signal. The proposed algorithm was compared to others HNR extraction algorithms based on spectral, cepstral and time domain methods, and using different performance measures.