A generalized multi-period mean-variance portfolio optimization with Markov switching parameters

In this paper, we deal with a generalized multi-period mean-variance portfolio selection problem with market parameters Subject to Markov random regime switchings. Problems of this kind have been recently considered in the literature for control over bankruptcy, for cases in which there are no jumps in market parameters (see [Zhu, S. S., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean variance formulation. IEEE Transactions on Automatic Control, 49, 447-457]). We present necessary and Sufficient conditions for obtaining an optimal control policy for this Markovian generalized multi-period meal-variance problem, based on a set of interconnected Riccati difference equations, and oil a set of other recursive equations. Some closed formulas are also derived for two special cases, extending some previous results in the literature. We apply the results to a numerical example with real data for Fisk control over bankruptcy Ill a dynamic portfolio selection problem with Markov jumps selection problem. (C) 2008 Elsevier Ltd. All rights reserved.

Identification of protein coding regions using the modified Gabor-wavelet transform

An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods based on the occurrence of specific patterns of nucleotides at coding regions have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically predefined window length required for a local analysis of a DNA region. We introduce a method based on a modified Gabor-wavelet transform (MGWT) for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods by using eukaryote data sets. The results show that MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors but also makes a tool available for detailed exploration of the nucleotide occurrence.

Analysis of Approaches to Controllership in Mater`s Thesis from Post-graduate Programs in Accounting Sciences

The objective of the Study is to analyze approaches in master`s thesis in Brazilian Post-Graduate Programs in Accounting Sciences in relation to Controllership, in terms of their conceptual, procedural and organizational aspects, as proposed by Borinelli (2006). The research is descriptive and it uses a quantitative approach. The sample consists of 26 master`s thesis which have the word ""Controllership"" in their titles. Resulting from analysis, in Perspective I (conceptual aspects), in which the elements of definition, object of study and relationship with other sciences were referenced, consensus among authors of the master`s thesis was not verified. In Perspective II (procedural aspects), which deals with activities and functions of Controllership by means of how they materialize as areas of knowledge within organizations, it was observed that the approach in the master`s thesis is quite differentiated in terms of the scope of activities. In relation to Perspective III (organizational aspects), there is also no consensus about what constitutes typical Controllership activities, but master`s thesis do include in the definition of Controllership the idea that it is a service or function of information. It was concluded that the approach to controllership, in terms of its conceptual, procedural and organizational aspects is similar to the elements proposed by Borinelli (2006).

Magnetic Investigation of CoFe(2)O(4) Nanoparticles Supported in Biocompatible Polymeric Microsphere

Magnetic investigation of spinel ferrite nanoparticles dispersed in biocompatible polymeric microspheres is reported in this study. X-ray diffraction data analysis confirms the presence of nanosized CoFe(2)O(4) particles (mean size of similar to 8 nm). This finding is corroborated by transmission electron microscopy micrographs. Magnetization isotherms suggest a spin disorder likely occurring at the nanoparticle`s surface. The saturation magnetization value is used to estimate particle concentration of 1.6 x 10(18) cm(-3) dispersed in the polymeric template. A T(1/2) dependence of the coercive field is determined in the low-temperature region (T < 30 K). The model of non-interacting mono-domains is used to estimate an effective magnetic anisotropy of K(eff) = 0.6 x 10(5) J/m(3). The K(eff) value we found is lower than the value reported for spherically-shaped CoFe(2)O(4) nanoparticles, though consistent with the low coercive field observed in the investigated sample.

Three-dimensional electrical impedance, tomography: A topology optimization approach

Electrical impedance tomography is a technique to estimate the impedance distribution within a domain, based on measurements on its boundary. In other words, given the mathematical model of the domain, its geometry and boundary conditions, a nonlinear inverse problem of estimating the electric impedance distribution can be solved. Several impedance estimation algorithms have been proposed to solve this problem. In this paper, we present a three-dimensional algorithm, based on the topology optimization method, as an alternative. A sequence of linear programming problems, allowing for constraints, is solved utilizing this method. In each iteration, the finite element method provides the electric potential field within the model of the domain. An electrode model is also proposed (thus, increasing the accuracy of the finite element results). The algorithm is tested using numerically simulated data and also experimental data, and absolute resistivity values are obtained. These results, corresponding to phantoms with two different conductive materials, exhibit relatively well-defined boundaries between them, and show that this is a practical and potentially useful technique to be applied to monitor lung aeration, including the possibility of imaging a pneumothorax.

Pressure-Frequency Sensing Subxiphoid Access System for Use in Percutaneous Cardiac Electrophysiology: Prototype Design and Pilot Study Results

We have designed, built, and tested an early prototype of a novel subxiphoid access system intended to facilitate epicardial electrophysiology, but with possible applications elsewhere in the body. The present version of the system consists of a commercially available insertion needle, a miniature pressure sensor and interconnect tubing, read-out electronics to monitor the pressures measured during the access procedure, and a host computer with user-interface software. The nominal resolution of the system is <0.1 mmHg, and it has deviations from linearity of <1%. During a pilot series of human clinical studies with this system, as well as in an auxiliary study done with an independent method, we observed that the pericardial space contained pressure-frequency components related to both the heart rate and respiratory rate, while the thorax contained components related only to the respiratory rate, a previously unobserved finding that could facilitate access to the pericardial space. We present and discuss the design principles, details of construction, and performance characteristics of this system.

Autoantibodies as predictors of biological therapy for early rheumatoid arthritis

Introduction: The association between serological markers with the need of biological therapy for early rheumatoid arthritis (ERA) is not known, with few available data addressing this question. Objectives: To prospectively evaluate a cohort of patients with ERA (less than 12 months of symptoms) in order to determine the possible association between serological markers (rheumatoid factor (RF), anti-cyclic citrullinated peptide antibodies (anti-CCP), and citrullinated anti-vimentin (anti-Sa) with parameters of therapeutic outcome (this later defined by the need of introducing biological therapy). Patients and methods: Forty patients with early RA were evaluated at the time of diagnosis and have been followed for 3 years, in use of standardized therapeutic treatment. Demographic and clinical data were recorded, as well as serology tests (ELISA) for RF (IgM, IgG and IgA), anti-CCP (CCP2, CCP3 and CCP3.1) and anti-Sa in the initial evaluation and at 3, 6, 12, 18, 24 and 36 months of follow-up. As outcomes of the RA development, the need or not for biological therapy during the follow-up period were considered. Comparisons were made through the Student t test, mixed-effects regression analysis and analysis of variance (significance level of 5%). Results: The mean age was 45 (+/- 12) years; a female predominance was observed (90%). At the time of diagnosis, RF was observed in 50% of cases (RF IgA - 42%, RF IgG - 30% and RF IgM - 50%), anti-CCP in 50% (no difference between CCP2, CCP3 and CCP3. 1) and anti-Sa in 10%. After 3 years, no change in the RF prevalence neither in the anti-CCP was observed, but the anti-Sa increased to 17.5% (p = 0.001). Biological therapy was necessary in 22.5% of patients. The mean RF IgA and anti-CCP 2 levels during the 3 years were higher among patients who needed biological therapy (p <0.05 for both). Conclusion: Higher titles of RF and anti-CCP over time were associated with the need for biological therapy.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.