Weak expansions: a distinctive feature of the business cycle in Latin America and the Caribbean

Using two standard cycle methodologies (Classical and Deviation Cycle) and a comprehensive sample of 83 countries worldwide, including all developing regions, we show that the Latin American and Caribbean cycle exhibits two distinctive features. First, and most importantly, its expansion performance is shorter and for the most par less imtense than that of the rest of the regions considered, and in particular than that of East Asia and the Pacific, East Asia and the Pacific's expansions last five years longer than those of LAC, and its output gain is 50% greater than that of LAC. Second, LAC tends to exhibit contractions that are not significantly different in terms of duration and amplitude than t those of other regions. Both these features imply that the complete Latin American and Caribbean cycle has, overall, the shortest duration and smallest amplitude in relation to other regions. The specificities of the Latin American and Caribbean cycle are not confined to the short run. These are also reflected in variables such as productivity and investment, which are linked to long-run growth. East Asia and the Pacific's cumulative gain in labor productivity during the expansionary phase is twice that of LAC. Moreover, the evidence also shows that the effects of the contraction in public investment surpass those of the expansion leading to a declining trend over the entire cycle. In this sense we suggest that policy analysis needs to increase its focus on the expansionary phase of the cycle. Improving our knowledge of the differences in the expansionary dynamics of countries and regions, can further our understanding of the differences in their rates of growth and levels of development. We also suggest that while, the management of the cycle affects the short-run fluctuations of economic activity and hence volatility, it is not trend neutral. Hence, the effects of aggregate demand management policies may be more persistent over time and less transitory than currently thought.

Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

Retention and processing methods of nasal prosthesis

Patients with congenital malformations, traumatic or pathological mutilation and maxillofacial developmental disorders can be restored aesthetically and emotionally by the production and use of facial prostheses. The aim of this study was to review the literature about the retention and processing methods of facial prostheses, and discuss their characteristics. A literature review on Medline (PubMed) database was performed by using the keywords maxillofacial prosthesis, silicone, resin, pigment, cosmetic, prosthetic nose, based on articles published from 1956 to 2010. Several methods of retention, from adhesives to the placement of implants, and different processing methods such as laser, CAD/CAM and rapid prototyping technologies have been reported. There are advantages and disadvantages of each procedure, and none can be classified as better compared to others.

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

Structural and Phylogenetic Studies with MjTX-I Reveal a Multi-Oligomeric Toxin - a Novel Feature in Lys49-PLA2s Protein Class

The mortality caused by snakebites is more damaging than many tropical diseases, such as dengue haemorrhagic fever, cholera, leishmaniasis, schistosomiasis and Chagas disease. For this reason, snakebite envenoming adversely affects health services of tropical and subtropical countries and is recognized as a neglected disease by the World Health Organization. One of the main components of snake venoms is the Lys49-phospholipases A2, which is catalytically inactive but possesses other toxic and pharmacological activities. Preliminary studies with MjTX-I from Bothrops moojeni snake venom revealed intriguing new structural and functional characteristics compared to other bothropic Lys49-PLA2s. We present in this article a comprehensive study with MjTX-I using several techniques, including crystallography, small angle X-ray scattering, analytical size-exclusion chromatography, dynamic light scattering, myographic studies, bioinformatics and molecular phylogenetic analyses.Based in all these experiments we demonstrated that MjTX-I is probably a unique Lys49-PLA2, which may adopt different oligomeric forms depending on the physical-chemical environment. Furthermore, we showed that its myotoxic activity is dramatically low compared to other Lys49-PLA2s, probably due to the novel oligomeric conformations and important mutations in the C-terminal region of the protein. The phylogenetic analysis also showed that this toxin is clearly distinct from other bothropic Lys49-PLA2s, in conformity with the peculiar oligomeric characteristics of MjTX-I and possible emergence of new functionalities inresponse to environmental changes and adaptation to new preys. © 2013 Salvador et al.

Nasal administration of liquid crystal precursor mucoadhesive vehicle as an alternative antiretroviral therapy

The purpose of this study was to develop a mucoadhesive stimuli-sensitive drug delivery system for nasal administration of zidovudine (AZT). The system was prepared by formulating a low viscosity precursor of a liquid crystal phase, taking advantage of its lyotropic phase behavior. Flow rheology measurements showed that the formulation composed of PPG-5-CETETH-20, oleic acid and water (55, 30, 15% w/w), denominated P, has Newtonian flow behavior. Polarized light microscopy (PLM) revealed that formulation P is isotropic, whereas its 1:1 (w/w) dilution with artificial nasal mucus (ANM) changed the system to an anisotropic lamellar phase (PD). Oscillatory frequency sweep analysis showed that PD has a high storage modulus (G′) at nasal temperatures. Measurement of the mucoadhesive force against excised porcine nasal mucosa or a mucin disk proved that the transition to the lamellar phase tripled the work of mucoadhesion. Ex vivo permeation studies across porcine nasal mucosa exhibited an 18-fold rise in the permeability of AZT from the formulation. The Weibull mathematical model suggested that the AZT is released by Fickian diffusion mechanisms. Hence, the physicochemical characterization, combined with ex vivo studies, revealed that the PPG-5-CETETH-20, oleic acid, and water formulation could form a mucoadhesive matrix in contact with nasal mucus that promoted nasal absorption of the AZT. For an in vivo assessment, the plasma concentrations of AZT in rats were determined by HPLC method following intravenous and intranasal administration of AZT-loaded P formulation (PA) and AZT solution, respectively, at a dose of 8 mg/kg. The intranasal administration of PA resulted in a fast absorption process (Tmax = 6.7 min). Therefore, a liquid crystal precursor formulation administered by the nasal route might represent a promising novel tool for the systemic delivery of AZT and other antiretroviral drugs. In the present study, the uptake of AZT absorption in the nasal mucosa was demonstrated, providing new foundations for clinical trials in patients with AIDS. © 2012 Elsevier B.V. All rights reserved.

Exterior orientation of CBERS-2B imagery using multi-feature control and orbital data

The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China-Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced. © 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Binary Bat Algorithm for Feature Selection

Feature selection aims to find the most important information to save computational efforts and data storage. We formulated this task as a combinatorial optimization problem since the exponential growth of possible solutions makes an exhaustive search infeasible. In this work, we propose a new nature-inspired feature selection technique based on bats behavior, namely, binary bat algorithm The wrapper approach combines the power of exploration of the bats together with the speed of the optimum-path forest classifier to find a better data representation. Experiments in public datasets have shown that the proposed technique can indeed improve the effectiveness of the optimum-path forest and outperform some well-known swarm-based techniques. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.

BCS: A Binary Cuckoo Search algorithm for feature selection

Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques. © 2013 IEEE.

Optimizing feature selection through binary charged system search

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Grafias não-convencionais da coda silábica nasal em dados de escrita de jovens e adultos em processo de alfabetização

Pós-graduação em Estudos Linguísticos - IBILCE