Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Protective effects of the galectin-1 protein on in vivo and in vitro models of ocular inflammation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in 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 in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

Propriedades físico-químicas de abacate submetido à secagem convectiva e desidratação osmótica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de equações para estimativa do VO2PICO pelo teste de 9 minutos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comprimidos de HPMC contendo nimesulida: preparação, caracterização e estudo dos mecanismos que regem a liberação do fármaco

The pharmaceutical innovations, such as the use of polymers to control drug release, create possibilities for a better action of the drug in the body, which causes a a more effective therapeutic effect and a safer treatment for the patient. In this work, were prepared and characterized matrix tablets of hydroxypropylmethylcellulose (HPMC) containing nimesulide as model drug to evaluate the performance as a controlled release system. HPMC, a cellulose ester, is a hydrophilic polymer that undergoes swelling, i.e., absorbs water and forms a gel layer controlling drug release. The characterization of powders was performed by analysis of particle size and morphology, density, compressibility index determination, flow properties and determination of swelling profile. The tablets were evaluated according to their physical parameters of quality and to the in vitro release of nimesulide, as well as the analysis of the mechanisms of drug release by appropriate mathematical models. The set of results showed that the HPMC/Nimesulide mixture exhibited satisfactory physical characteristics (size, shape, density and flow). The release profile demonstrated an effective control upon drug release in enteric environment and presented more correlation with Korsmeyer-Peppas’ and Weibull’s mathematical models, indicating that the release of nimesulide occurs through the relaxation of the polymer chains

Extração de feições retas e cálculo de entidades pontuais a partir de dados LASER para o ajustamento relativo de faixas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modelo matemático para determinação do custo e produção de energia na cultura da cana-de-açúcar

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelos de previsão de ocorrência de adultos de diatraea saccharalis (Fabricius, 1794) (Lepidoptera: Crambidae) em cana-de-açúcar (Saccharum spp.)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Applied models to biodegradation kinetics of lubricant and vegetable oils in wastewater

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Egg production curve fitting using nonlinear models for selected and nonselected lines of White Leghorn hens

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic parameter estimates for live weight and daily live weight gain obtained for Nellore bulls in a test station using different models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)