The correlation between thermal comfort in buildings and fashion products

This article is about thermal comfort in the wearable product. The research correlates fashion and architecture, in so far as it elects the brise soleil - an architectural element capable of regulating temperature and ventilation inside buildings - as a study referential, in trying to transpose and adapt its mechanisms to the wearable apparel.

Sliding Vector Fields via Slow-Fast Systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

A New and Fast Technique to Generate Offspring after Germ Cells Transplantation in Adult Fish: The Nile Tilapia (Oreochromis niloticus) Model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Electrospray ionization mass spectrometry screening of piperidine alkaloids from Senna spectabilis (Fabaceae) extracts: Fast identification of new constituents and co-metabolites

The fragmentation pattern of a homologous series of piperidine alkaloids isolated from S. spectabilis was investigated using electrospray ionization tandem mass spectrometry (ESI-MS/MS). The ESI-MS and ESI-MS/MS analyses of EtOH extracts and fractions from flowers and fruits of S. spectabilis allowed to elucidate the structures of four new compounds. The identification of these co-metabolites, based on the fragmentation patterns of previously isolated compounds, and further confirmed by accurate mass spectrometry defines this technique as a powerful tool to determine the metabolomic profile of species which has pharmacological importance. ©2005 Sociedade Brasileira de Química.

Fast preparative separation of flavones from capitula of Eriocaulon ligulatum (Vell.) L.B.Smith (Eriocaulaceae) by High-speed countercurrent chromatography (HSCCC)

High-speed countercurrent chromatography (HSCCC) is a leading method for the fast separation of natural products from plants. It was used for the preparative isolation of two flavone monoglucosides present in the capitula of Eriocaulon ligulatum (Veil.) L.B.Smith (Eriocaulaceae). This species, known locally as botão-dourado, is exported to Europe, Japan and North America as an ornamental species, constituting an important source of income for the local population of Minas Gerais State, Brazil. The solvent system, optimized in tests prior to the HSCCC run, consisted of the two phases of the mixture ethyl acetate: n-propanol: water (140:8:80, v/v/v), which led to the successful separation of 6-methoxyluteolin-7-O-β-D-allopyranoside and 6-methoxyapigenin-7-O-β-D-allopyranoside in only 3 hours. The two flavonoids were identified by NMR (1-D and 2-D) and ESI-MS, comparing their spectra with published data.

Detecção de prováveis deformações neotectônicas no Vale do Rio do Peixe, Região Ocidental Paulista, mediante aplicaçao de Índices RDE (Relac̈ão Declividade-Extensão) em segmentos de drenagem

Running water is one of the most important of all the physical processes which fashion the landscape, allowing gravity to operate along the valley floors. Besides this, the streams show a fast adjustment to the crustal deformations, even to the most gentle ones. This geologic behavior turns them a potential tool for neotectonic studies, specially the analysis of morphotnetric parameters associated with hydraulic gradient and discharge, this second factor being directly proportional to the extension of the streams. Both elements, gradient and stream length, can be combined in the SL index. The purpose of this paper is to show the RDE index application in the neotectonics analysis of the Rio do Peixe hydrographic basin and to compare the obtained values with the geologic basement incised by the streams. This basement encompasses Cretaceous sedimentary rocks of post-Serra Oeral Formation magmatism (Caiuá and Bauru groups) and Quaternary deposits that include chiefly recent alluvial plains and some Pleistocene terrace deposits. In the final part of this paper, an attempt is made in order to correlate the RDE results and the neotectonic framework admitted to this portion of the São Paulo State territory, as well as with field geologic, seismologic and paleoseismologic known elements. The results indicate the presence of two groups of anomalies: The first set corresponds to the Marília-Exaporã Plateau border, and the second one, located in the central portion of the hydrographic basin, is correlated to the Presidente Prudente seimogenic zone.

Actual Brazilian habitations in wood: A technology in development or a question of fashion?

Over the past few years there have significantly been increased the articles that approach the constructions in wood or with structure in wood in the specialized Brazilian magazines. This increase brings up indications that the incorporated values to these habitations are modifying, however it is not so simple to conclude that these issues can be associated to the development of incorporated technologies to the constructive system. The work presents, firstly, a survey performed in these publications that had the objective to verify which the constructive systems in wood is being more executed, under which cultural and technician standards. From that survey it was performed a study of the habitations constructed in mixing system whose structures are timbers and walls in masonry. The aesthetic and cultural questions involved are argued considering mainly that these habitations belong to a social class whose purchasing power increased.

Microwave-induced fast decalcification of rat bone for electron microscopic analysis: An ultrastructural and cytochemical study

Bone decalcification is a time-consuming process. It takes weeks and preservation of the tissue structure depends on the quality and velocity of the demineralization process. In the present study, a decalcification methodology was adapted using microwaving to accelerate the decalcification of rat bone for electron microscopic analysis. The ultrastructure of the bone decalcified by microwave energy was observed. Wistar rats were perfused with paraformaldehyde and maxillary segments were removed and fixed in glutaraldehyde. Half of specimens were decalcified by conventional treatment with immersion in Warshawsky solution at 4oC during 45 days, and the other half of specimens were placed into the beaker with 20 mL of the Warshawsky solution in ice bath and thereafter submitted to irradiation in a domestic microwave oven (700 maximum power) during 20 s/350 W/±37°C. In the first day, the specimens were irradiated 9 times and stored at 40°C overnight. In the second day, the specimens were irradiated 20 times changing the solution and the ice after each bath. After decalcification, some specimens were postfixed in osmium tetroxide and others in osmium tetroxide and potassium pyroantimonate. The specimens were observed under transmission electron microscopy. The results showed an increase in the decalcification rate in the specimens activated by microwaving and a reduction of total experiment time from 45 days in the conventional method to 48 hours in the microwave-aided method.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.