Crescimento, partição de fotoassimilados e produção de rizomas em taro cultivado sob sombreamento artificial

O cultivo associado é uma alternativa para culturas de ciclo longo e propriedades com limitação de área. Todavia, para o estabelecimento de associações, há necessidade de conhecer a tolerância das espécies ao sombreamento. O trabalho objetivou avaliar o crescimento de parte aérea, partição de fotoassimilados e produção de rizomas em plantas de taro 'Japonês' cultivadas sob intensidades e períodos de sombreamento artificial. Utilizou-se o delineamento de blocos ao acaso, com 13 tratamentos e quatro repetições. Os tratamentos foram constituídos de quatro intensidades de sombreamento (controle = pleno sol; 18; 30 e 50% de sombra, mantidos durante o ciclo todo), além da implementação das intensidades de sombra de 18; 30 e 50%, em três períodos (inicial = 0 a 3; intermediário = 3 a 6 e final = 6 a 9 meses). Aos 60; 90; 120; 150; 180; 210; 240 e 270 dias após o plantio (dat), avaliou-se crescimento de planta e partição de massa entre órgãos e aos 270 dat a produção de rizomas. Plantas sob sombreamento, sobretudo nas maiores intensidades e durante o ciclo todo, apresentaram maior produção de biomassa de parte aérea e de rizomas-mãe e filhos pequenos, em detrimento de rizomas-filho grandes, médios e comerciáveis. A intensidade de 18% de sombra, durante todo o ciclo ou nos períodos inicial e intermediário, foi a que menos afetou o desenvolvimento das plantas e produção de biomassa de rizomas-filho comerciáveis.

Técnicas de inteligência artificial para a geração dinâmica de set points para uma coluna de destilação

Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Contribuição para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)

This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

Uma arquitetura para sistemas supervisórios industriais e sua aplicação em processos de elevação artificial de petróleo

The using of supervision systems has become more and more essential in accessing, managing and obtaining data of industrial processes, because of constant and frequent developments in industrial automation. These supervisory systems (SCADA) have been widely used in many industrial environments to store process data and to control the processes in accordance with some adopted strategy. The SCADA s control hardware is the set of equipments that execute this work. The SCADA s supervision software accesses process data through the control hardware and shows them to the users. Currently, many industrial systems adopt supervision softwares developed by the same manufacturer of the control hardware. Usually, these softwares cannot be used with other equipments made by distinct manufacturers. This work proposes an approach for developing supervisory systems able to access process information through different control hardwares. An architecture for supervisory systems is first defined, in order to guarantee efficiency in communication and data exchange. Then, the architecture is applied in a supervisory system to monitor oil wells that use distinct control hardwares. The implementation was modeled and verified by using the formal method of the Petri networks. Finally, experimental results are presented to demonstrate the applicability of the proposed solution

Análise de desempenho da rede neural artificial do tipo multilayer perceptron na era multicore

Artificial neural networks are usually applied to solve complex problems. In problems with more complexity, by increasing the number of layers and neurons, it is possible to achieve greater functional efficiency. Nevertheless, this leads to a greater computational effort. The response time is an important factor in the decision to use neural networks in some systems. Many argue that the computational cost is higher in the training period. However, this phase is held only once. Once the network trained, it is necessary to use the existing computational resources efficiently. In the multicore era, the problem boils down to efficient use of all available processing cores. However, it is necessary to consider the overhead of parallel computing. In this sense, this paper proposes a modular structure that proved to be more suitable for parallel implementations. It is proposed to parallelize the feedforward process of an RNA-type MLP, implemented with OpenMP on a shared memory computer architecture. The research consistes on testing and analizing execution times. Speedup, efficiency and parallel scalability are analyzed. In the proposed approach, by reducing the number of connections between remote neurons, the response time of the network decreases and, consequently, so does the total execution time. The time required for communication and synchronization is directly linked to the number of remote neurons in the network, and so it is necessary to investigate which one is the best distribution of remote connections

Simulador computacional para poços de petróleo com método de elevação artificial por bombeio mecânico

This work proposes a computer simulator for sucker rod pumped vertical wells. The simulator is able to represent the dynamic behavior of the systems and the computation of several important parameters, allowing the easy visualization of several pertinent phenomena. The use of the simulator allows the execution of several tests at lower costs and shorter times, than real wells experiments. The simulation uses a model based on the dynamic behavior of the rod string. This dynamic model is represented by a second order partial differencial equation. Through this model, several common field situations can be verified. Moreover, the simulation includes 3D animations, facilitating the physical understanding of the process, due to a better visual interpretation of the phenomena. Another important characteristic is the emulation of the main sensors used in sucker rod pumping automation. The emulation of the sensors is implemented through a microcontrolled interface between the simulator and the industrial controllers. By means of this interface, the controllers interpret the simulator as a real well. A "fault module" was included in the simulator. This module incorporates the six more important faults found in sucker rod pumping. Therefore, the analysis and verification of these problems through the simulator, allows the user to identify such situations that otherwise could be observed only in the field. The simulation of these faults receives a different treatment due to the different boundary conditions imposed to the numeric solution of the problem. Possible applications of the simulator are: the design and analysis of wells, training of technicians and engineers, execution of tests in controllers and supervisory systems, and validation of control algorithms

Comportamento de chamamento de Thyrinteina arnobia (Stoll) (Lepidoptera: Geometridae) em Psidium guajava, Eucalyptus grandis e em dieta artificial

Este estudo teve como objetivos determinar e comparar o efeito de três dietas: Psidium guajava, Eucalyptus grandis e dieta artificial no comportamento de chamamento e no padrão temporal do comportamento de chamamento de fêmeas virgens de Thyrinteina arnobia (Stoll). Foram analisados os parâmetros: tempo médio para o início do chamamento, número médio de chamamento, tempo médio de cada chamamento e tempo médio total de chamamento. Os bioensaios relacionados ao comportamento de chamamento de fêmeas virgens foram realizados durante seis escotofases consecutivas e as observações tomadas a cada 5 min., a 22 ± 1ºC, 70 ± 5% de UR e 10h de escotofase. As fêmeas, independente da dieta analisada, apresentaram duas posições diferentes de chamamento. A maioria das fêmeas virgens iniciou o chamamento na 1ª hora da 1ª escotofase. O padrão de chamamento da fêmea individualizada foi característico de um padrão contínuo. O tipo de dieta oferecida na fase larval influenciou o início do comportamento de chamamento (pré-chamamento) e o tempo médio total de chamamento. O tipo de dieta também alterou a coloração da glândula de feromônio.

Sites of Defoliation by Atta sexdens rubropilosa (Hymenoptera, Formicidae) in Artificial Plants

This study aimed to achieve a better understanding about the foraging behavior of leaf-cutter ant (Atta sexdens rubropilosa Forel) workers with respect to defoliation sites in plants. To accomplish that, artificial plants 70 cm in height were prepared and divided into four levels (heights), having natural plant leaves attached to them. Evaluations during the bioassays included the number of leaves dropped by the ants, as well as the percentage of plant mass removed. In all replicates, it became evident that the most exploited plant site is the apical region, which significantly differed from other plant levels.

Effectiveness of Photogem (R) activated by LED on the decontamination of artificial carious bovine dentin

The aim of this study was the evaluation of the effectiveness of photodynamic therapy on the decontamination of artificially induced carious bovine dentin, using Photoge(R) as the photosensitizer agent and an LED device as a light source. Dentin samples obtained from bovine incisors were immersed in sterile broth supplemented by Lactobacillus acidophillus 10(8) colony formation units (CFU) and Streptococcus mutans 10 8 CFU. Different concentrations of photosensitizer, PA = 1 mg/ml, PB = 2 mg/ml, and PC = 3 mg/ml, and two fluences, D = 24 J/cm(2) and D = 48 J/cm(2), were investigated. After CFU counting per milligram of carious dentin and statistical analysis, we observed that the photodynamic therapy (PDT) parameters used were effective for bacterial reduction in the in vitro model under study. The best result was achieved with the application of Photoge(R) at 2 mg/ml and photoactivated under 24 J/cm(2) showing a survival factor of 0.14. At higher photosensitizer concentrations, a higher dark toxicity was observed. We propose a simple mathematical expression for the determination of PDT parameters of photosensitizer concentration and light fluence for different survival factor values. Since LED devices are simpler and cheaper compared to laser systems, it would be interesting to verify their efficacy as a light source in photodynamic therapy for the decontamination of carious dentin.

Susceptibilidade de Rhodnius neglectus, Rhodnius robustus e Triatoma infestans (Hemiptera, Reduviidae, Triatominae) à infecção por duas cepas de Trypanosoma cruzi (Kinetoplastidae, Trypanosomatidae) utilizando xenodiagnóstico artificial

A susceptibilidade de ninfas de 3º estádio de Rhodnius neglectus, R. robustus e Triatoma infestans às cepas Y e AMJM de Trypanosoma cruzi foi verificada utilizando xenodiagnóstico artificial. Para a leitura do xenodiagnóstico, as fezes dos triatomíneos foram examinadas a cada dois dias, a partir do 5º até o 31º dia pós infecção, pela técnica de compressão abdominal. Os resultados mostraram diferenças na susceptibilidade dos triatomíneos para as duas cepas estudadas e o período ótimo de leitura variou do 11º ao 19º dias para a cepa Y e do 11º ao 15º dias para a cepa AMJM. Também, pôde-se concluir que para a cepa Y, as três espécies de triatomíneos demonstraram boa susceptibilidade, enquanto para a cepa AMJM, a melhor susceptibilidade foi observada com R. neglectus, seguida pelo T. infestans e R. robustus.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Artificial neural networks applied in study of atmospheric parameters to high voltage substations concerning lightning

This gaper demonstrates that artificial neural networks can be used effectively for estimation of parameters related to study of atmospheric conditions to high voltage substations design. Specifically, the neural networks are used to compute the variation of electrical field intensity and critical disruptive voltage in substations taking into account several atmospheric factors, such as pressure, temperature, humidity, so on. Examples of simulation of tests are presented to validate the proposed approach. The results that were obtained by experimental evidences and numerical simulations allowed the verification of the influence of the atmospheric conditions on design of substations concerning lightning.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.