Evoluções nas modelagens de substratos artificiais (metamateriais) com práticas experimentais em antenas de microfita

Esse trabalho tem como objetivo apresentar configurações de substratos dielétricos inovadores projetados e fabricados a partir de estruturas metamateriais. Para isso, são avaliados diversos fatores que podem influenciar no seu desempenho. A princípio, foi feito um levantamento bibliográfico a respeito dos temas, que estão relacionados com as pesquisas sobre: materiais dielétricos, metamateriais e interferometria óptica. São estudados, pesquisados e desenvolvidos dois projetos experimentais propostos, que comprovam a eficiência de métodos, para se alcançar a permeabilidade magnética negativa na formação de metamateriais. O primeiro projeto é a produção de uma nova estrutura, com u anel ressoador triangular equilateral (Split Equilateral Triangle Resonator - SETR). O segundo projeto: aplica os princípios da interferometria óptica, especialmente, com o interferômetro de Fabry-Perot. Técnicas para obtenção dos dispositivos que complementam a placa metamaterial como substrato foram pesquisadas na literatura e exemplificadas principalmente por meio de simulações e medições. Foram feitas comparações, simulações e medições de estruturas convencionais e especiais. As experiências se concentram nas evoluções e modelagens de substratos metamateriais com aplicações em antenas de microfita. As melhorias de alguns parâmetros de desempenho de antenas também são relatadas. As simulações das antenas foram feitas nos programas computacionais comerciais. Os resultados medidos foram obtidos com um analisador vetorial de redes da Rhode and Schwarz modelo ZVB 14.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development

Estudos e projetos de filtro interdigital em microfita para aplicação prática ao transponder do satélite ITASAT

This work shows a theoretical analysis together with numerical and experimental results of transmission characteristics from the microstrip bandpass filters with different geometries. These filters are built over isotropic dielectric substrates. The numerical analysis is made by specifical commercial softwares, like Ansoft Designer and Agilent Advanced Design System (ADS). In addition to these tools, a Matlab Script was built to analyze the filters through the Finite-Difference Time-Domain (FDTD) method. The filters project focused the development of the first stage of filtering in the ITASAT s Transponder receptor, and its integration with the others systems. Some microstrip filters architectures have been studied, aiming the viability of implementation and suitable practical application for the purposes of the ITASAT Project due to its lowspace occupation in the lower UHF frequencies. The ITASAT project is a Universityexperimental project which will build a satellite to integrate the Brazilian Data Collect System s satellite constellation, with efforts of many Brazilian institutes, like for example AEB (Brazilian Spatial Agency), ITA (Technological Institute of Aeronautics), INPE/CRN (National Institute of Spatial Researches/Northeastern Regional Center) and UFRN (Federal University of Rio Grande do Norte). Comparisons were made between numerical and experimental results of all filters, where good agreements could be noticed, reaching the most of the objectives. Also, post-work improvements were suggested.

Atividades experimentais no ensino de biologia em escolas públicas do estado do Rio Grande do Norte, Brasil: caracterização geral e concepção de professores

Recent research has revealed that the majority of Biology teachers believe the practice of experimental activities as a didactical means would be the solution for the improvement of the Biology teaching-learning process. There are, however, studies which signal the lack of efficiency in such practice lessons as far as building scientific knowledge is concerned. It is also said that despite the enthusiasm on the teachers‟ part, such classes are rarely taught in high school. Several studies point pedagogical difficulties as well as nonexistence of a minimal infrastructure needed in laboratories as cause of low frequency in experimental activities. The poor teacher performance in terms of planning and development of classes; the large number of students per class; lack of financial stimulus for teachers are other reasons to be taken into account among others, in which can also be included difficulties of epistemological nature. That means an unfavorable eye of the teacher towards experimental activities. Our study aimed to clarify if such scenario is generalized in high schools throughout the state of Rio Grande do Norte Brazil. During our investigation a sample of twenty teaching institutions were used. They were divided in two groups: in the first group, five IFRN- Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte schools. Two of those in Natal, and the other three from the country side. The second group is represented by fifteen state schools belonging to the Natal metropolitan area. The objectives of the research were to label schools concerning laboratory facilities; to identify difficulties pointed by teachers when performing experiment classes, and to become familiar with the conceptions of the teachers in regarding biology experiment classes. To perform such task, a questionnaire was used as instrument of data collecting. It contained multiple choice, essay questions and a semi-structured interview with the assistance of a voice recorder. The data analysis and the in loco observation allowed the conclusion that the federal schools do present better facilities for the practice of experimental activities when compared to state schools. Another aspect pointed is the fact that teachers of federal schools have more time available for planning the experiments; they are also better paid and are given access a career development, which leads to better salaries. All those advantages however, do not show a significantly higher frequency regarding the development of experiments when compared to state school teachers. Both teachers of federal and state schools pointed infra-structure problems such as the availability of reactants, equipments and consumption supplies as main obstacle to the practice of experiments in biology classes. Such fact leads us to conclude that maybe there are other problems not covered by the questionnaire such as poor ability to plan and execute experimental activities. As far as conceptions about experimental activities, it was verified in the majority of the interviewees a inductive-empiric point of view of science possibly inherited during their academic formation and such point of view reflected on the way they plan and execute experiments with students

Estudos de adsorção de tetraciclina e cromoglicato em partículas de quitosana

Among the polymers that stand out most in recent decades, chitosan, a biopolymer with physico-chemical and biological promising properties has been the subject of a broad field of research. Chitosan comes as a great choice in the field of adsorption, due to their adsorbents properties, low cost and abundance. The presence of amino groups in its chain govern the majority of their properties and define which application a sample of chitosan may be used, so it is essential to determine their average degree of deacetylation. In this work we developed kinetic and equilibrium studies to monitor and characterize the adsorption process of two drugs, tetracycline hydrochloride and sodium cromoglycate, in chitosan particles. Kinetic models and the adsorption isotherms were applied to the experimental data. For both studies, the zeta potential analyzes were also performed. The adsorption of each drug showed distinct aspects. Through the studies developed in this work was possible to describe a kinetic model for the adsorption of tetracycline on chitosan particles, thus demonstrating that it can be described by two kinetics of adsorption, one for protonated tetracycline and another one for unprotonated tetracycline. In the adsorption of sodium cromoglycate on chitosan particles, equilibrium studies were developed at different temperatures, allowing the determination of thermodynamic parameters

Estudos de adsorção de tetraciclina em partículas de quitosana

Due to its physico-chemical and biological properties, related to the abundance and low cost of raw material, chitosan has been recognized as a material of wide application in various fields, such as in drug delivery systems. Many of these properties are associated with the presence of amino groups in its polymer chain. A proper determination of these amino groups is very important, in order to properly specify if a given chitosan sample can be used in a particular application. Thus, in this work, initially, a comparison between the determination of the deacetylation degree by conductometry and elemental analysis was carried out using a detailed analysis of error propagation. It was shown that the conductometric analysis resulted in a simple and safe method for the determining the degree of deacetylation of chitosan. Subsequently, experiments were performed to monitor and characterize the adsorption of tetracycline on chitosan particles through kinetic and equilibrium studies. The main models of kinetics and adsorption isotherms, widely used to describe the adsorption on wastewater treatment systems and the drug loading, were used to treat the experimental data. Firstly, it was shown that an apparent linear t/q(t) × t relationship did not imply in a pseudo-second-order adsorption kinetics, differently of what has been repeatedly reported in the literature. It was found that this misinterpretation can be avoided by using non-linear regression. Finally, the adsorption of tetracycline on chitosan particles was analyzed using insights obtained from theoretical analysis, and the parameters generated were used to analyze the kinetics of adsorption, the isotherm of adsorption and to ropose a mechanism of adsorption

Estudos de sorção de um corante aniônico modelo em partículas de quitosana reticulada

Textile activity results in effluents with a variety of dyes. Among the several processes for dye-uptaking from these wastewaters, sorption is one of the most effective methods, chitosan being a very promising alternative for this end. The sorption of Methyl Orange by chitosan crosslinked particles was approached using equilibrium and kinetic analyses at different pH s. Besides the standard pseudo-order analysis normally effectuated (i.e. pseudo-first-order and pseudo-second-order), a novel approach involving a pseudo-nth-order kinetics was used, nbeing determined via non-linear regression, using the Levenberg-Marquardt method. Zeta potential measurements indicated that electrostatic interactions were important for the sorption process. Regarding equilibrium experiments, data were well fitted to a hybrid Langmuir-Freundlich isotherm, and estimated Gibbs free energy of adsorption as a function of mass of dye per area of chitosan showed that the process of adsorption becomes more homogeneous as the pH of the continuous phase decreased. Considering the kinetics of sorption, although a pseudo-nth-order description yielded good fits, a kinetic equation involving diffusion adsorption phenomena was found to be more consistent in terms of a physicochemical description of the sorption process

Cálculo das incertezas climáticas sobre América do Sul utilizando modelos do CMIP5: aferição através das redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model