Anodic porous alumina structural characteristics study based on SEM image processing and analysis

The present work reports the porous alumina structures fabrication and their quantitative structural characteristics study based on mathematical morphology analysis by using the SEM images. The algorithm used in this work was implemented in 6.2 MATLAB software. Using the algorithm it was possible to obtain the distribution of maximum, minimum and average radius of the pores in porous alumina structures. Additionally, with the calculus of the area occupied by the pores, it was possible to obtain the porosity of the structures. The quantitative results could be obtained and related to the process fabrication characteristics, showing to be reliable and promising to be used to control the pores formation process. Then, this technique could provide a more accurate determination of pore sizes and pores distribution. (C) 2008 Elsevier Ltd. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Investigation of AISI 1040 steel corrosion H(2)S solution containing chloride ions by digital image processing coupled with in electrochemical techniques

This paper presents a study of AISI 1040 steel corrosion in aqueous electrolyte of acetic acid buffer containing 3.1 and 31 x 10(-3) mol dm(-3) of Na(2)S in both the presence and absence of 3.5 wt.% NaCl. This investigation of steel corrosion was carried out using potential polarization, and open-circuit and in situ optical microscopy. The morphological analysis and classification of types of surface corrosion damage by digital image processing reveals grain boundary corrosion and shows a non-uniform sulfide film growth, which occurs preferentially over pearlitic grains through successive formation and dissolution of the film. (C) 2011 Elsevier Ltd. All rights reserved.

Vegetable-origin foam employed in dye extraction in tanning and leather processing facilities

This study addressed the use of conventional and vegetable origin polyurethane foams to extract C. I. Acid Orange 61 dye. The quantitative determination of the residual dye was carried out with an UV/Vis absorption spectrophotometer. The extraction of the dye was found to depend on various factors such as pH of the solution, foam cell structure, contact time and dye and foam interactions. After 45 days, better results were obtained for conventional foam when compared to vegetable foam. Despite presenting a lower percentage of extraction, vegetable foam is advantageous as it is considered a polymer with biodegradable characteristics.

Pitfalls of artificial grouping and stratification of scientific journals based on their Impact Factor: a case study in Brazilian Zoology

The present contribution explores the impact of the QUALIS metric system for academic evaluation implemented by CAPES (Coordination for the Development of Personnel in Higher Education) upon Brazilian Zoological research. The QUALIS system is based on the grouping and ranking of scientific journals according to their Impact Factor (IF). We examined two main points implied by this system, namely: 1) its reliability as a guideline for authors; 2) if Zoology possesses the same publication profile as Botany and Oceanography, three fields of knowledge grouped by CAPES under the subarea "BOZ" for purposes of evaluation. Additionally, we tested CAPES' recent suggestion that the area of Ecology would represent a fourth field of research compatible with the former three. Our results indicate that this system of classification is inappropriate as a guideline for publication improvement, with approximately one third of the journals changing their strata between years. We also demonstrate that the citation profile of Zoology is distinct from those of Botany and Oceanography. Finally, we show that Ecology shows an IF that is significantly different from those of Botany, Oceanography, and Zoology, and that grouping these fields together would be particularly detrimental to Zoology. We conclude that the use of only one parameter of analysis for the stratification of journals, i.e., the Impact Factor calculated for a comparatively small number of journals, fails to evaluate with accuracy the pattern of publication present in Zoology, Botany, and Oceanography. While such simplified procedure might appeals to our sense of objectivity, it dismisses any real attempt to evaluate with clarity the merit embedded in at least three very distinct aspects of scientific practice, namely: productivity, quality, and specificity.

Multidisciplinary optimization of a catamaran's hydrofoil design with dynamic response surface and distributive processing

This paper presents a rational approach to the design of a catamaran's hydrofoil applied within a modern context of multidisciplinary optimization. The approach used includes the use of response surfaces represented by neural networks and a distributed programming environment that increases the optimization speed. A rational approach to the problem simplifies the complex optimization model; when combined with the distributed dynamic training used for the response surfaces, this model increases the efficiency of the process. The results achieved using this approach have justified this publication.

Crystallization, data collection and data processing of maltose-binding protein (MalE) from the phytopathogen Xanthomonas axonopodis pv. citri

Maltose-binding protein is the periplasmic component of the ABC transporter responsible for the uptake of maltose/maltodextrins. The Xanthomonas axonopodis pv. citri maltose-binding protein MalE has been crystallized at 293 Kusing the hanging-drop vapour-diffusion method. The crystal belonged to the primitive hexagonal space group P6(1)22, with unit-cell parameters a = 123.59, b = 123.59, c = 304.20 angstrom, and contained two molecules in the asymetric unit. It diffracted to 2.24 angstrom resolution.

Modulation of Auditory and Visual Processing by Delta-9-Tetrahydrocannabinol and Cannabidiol: an fMRI Study

Although the effects of cannabis on perception are well documented, little is known about their neural basis or how these may contribute to the formation of psychotic symptoms. We used functional magnetic resonance imaging (fMRI) to assess the effects of Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) during visual and auditory processing in healthy volunteers. In total, 14 healthy volunteers were scanned on three occasions. Identical 10mg THC, 600mg CBD, and placebo capsules were allocated in a balanced double-blinded pseudo-randomized crossover design. Plasma levels of each substance, physiological parameters, and measures of psychopathology were taken at baseline and at regular intervals following ingestion of substances. Volunteers listened passively to words read and viewed a radial visual checkerboard in alternating blocks during fMRI scanning. Administration of THC was associated with increases in anxiety, intoxication, and positive psychotic symptoms, whereas CBD had no significant symptomatic effects. THC decreased activation relative to placebo in bilateral temporal cortices during auditory processing, and increased and decreased activation in different visual areas during visual processing. CBD was associated with activation in right temporal cortex during auditory processing, and when contrasted, THC and CBD had opposite effects in the right posterior superior temporal gyrus, the right-sided homolog to Wernicke`s area. Moreover, the attenuation of activation in this area (maximum 61, -15, -2) by THC during auditory processing was correlated with its acute effect on psychotic symptoms. Single doses of THC and CBD differently modulate brain function in areas that process auditory and visual stimuli and relate to induced psychotic symptoms. Neuropsychopharmacology (2011) 36, 1340-1348; doi:10.1038/npp.2011.17; published online 16 March 2011

Nop53p interacts with 5.8S rRNA co-transcriptionally, and regulates processing of pre-rRNA by the exosome

In eukaryotes, pre-rRNA processing depends on a large number of nonribosomal trans-acting factors that form intriguingly organized complexes. One of the early stages of pre-rRNA processing includes formation of the two intermediate complexes pre-40S and pre-60S, which then form the mature ribosome subunits. Each of these complexes contains specific pre-rRNAs, ribosomal proteins and processing factors. The yeast nucleolar protein Nop53p has previously been identified in the pre-60S complex and shown to affect pre-rRNA processing by directly binding to 5.8S rRNA, and to interact with Nop17p and Nip7p, which are also involved in this process. Here we show that Nop53p binds 5.8S rRNA co-transcriptionally through its N-terminal region, and that this protein portion can also partially complement growth of the conditional mutant strain Delta nop53/GAL:NOP53. Nop53p interacts with Rrp6p and activates the exosome in vitro. These results indicate that Nop53p may recruit the exosome to 7S pre-rRNA for processing. Consistent with this observation and similar to the observed in exosome mutants, depletion of Nop53p leads to accumulation of polyadenylated pre-rRNAs.

Utp25p, a nucleolar Saccharomyces cerevisiae protein, interacts with U3 snoRNP subunits and affects processing of the 35S pre-rRNA

In eukaryotes, pre-rRNA processing depends on a large number of nonribosomal trans-acting factors that form intriguingly organized complexes. Two intermediate complexes, pre-40S and pre-60S, are formed at the early stages of 35S pre-rRNA processing and give rise to the mature ribosome subunits. Each of these complexes contains specific pre-rRNAs, some ribosomal proteins and processing factors. The novel yeast protein Utp25p has previously been identified in the nucleolus, an indication that this protein could be involved in ribosome biogenesis. Here we show that Utp25p interacts with the SSU processome proteins Sas10p and Mpp10p, and affects 18S rRNA maturation. Depletion of Utp25p leads to accumulation of the pre-rRNA 35S and the aberrant rRNA 23S, and to a severe reduction in 40S ribosomal subunit levels. Our results indicate that Utp25p is a novel SSU processome subunit involved in pre-40S maturation.

An Alternative Optical Method for Acoustic Resonance Detection in HID Lamps

Acoustic resonances are observed in high-pressure discharge lamps operated with ac input modulated power frequencies in the kilohertz range. This paper describes an optical resonance detection method for high-intensity discharge lamps using computer-controlled cameras and image processing software. Experimental results showing acoustic resonances in high-pressure sodium lamps are presented.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.