21 resultados para Free spectral range (FSR)


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The aggregation behavior of the non-ionic surfactant Renex-100 in aqueous solutions and mesophases was evaluated by SAXS in a wide range of concentrations, between 20 and 30 °C. Complementary, water interactions were defined by DSC curves around 0°C. SAXS showed that the system undergoes the following phase transitions, from diluted to concentrated aqueous solutions: 1) isotropic solution of Renex aggregates; 2) hexagonal mesophase; 3) lamellar mesophase; and 4) isotropic solution. DSC analysis indicated the presence of interfacial water above 70wt%, which agreed with the segregation of free water to form the structural mesophases observed by SAXS bellow this concentration.

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Remotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. The aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in Rondônia, including (1) areas of mid-size farms, (2) fish-bone settlements and (3) a gradient of forest and Cerrado (Brazilian savannah). Comparison with a reference map based on the kappa statistics resulted in good to superior indicators (best results - K-means: k=0.68; k=0.77; k=0.64 and MaxVer: k=0.71; k=0.89; k=0.70 respectively for three areas mentioned). Results show that choosing a specific algorithm requires to take into account both its capacity to discriminate among various spectral signatures under different landscape patterns as well as a cost/benefit analysis considering the different steps performed by the operator performing a land cover/use map. it is suggested that a more systematic assessment of several options of implementation of a specific project is needed prior to beginning a land use/cover mapping job.

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The difficulty in adapting European dairy cows breeds in Brazil affect considerably the milk production sector. Brazilian climatic conditions are not totally favorable and the development of new tecnologies is needed for the animals express their genetic potential, as well as their best feed conversion. An economical analysis of the applied investment in the free-stall climatization equipment in dairy housing, for estimating studies related to profit, possibility of return investment as well as time for this return is necessary. The objective of this research was to evaluate the influence of climatization investment in the milk production process and analyze the economical aspect of this investment. There were used 470 high productive dairy cows with genetic and morphologic homogeneous characteristics, and analyzed in two similar periods. Investment calculations were done using Excell®. The results were satisfactory and the invested capital was proved to return to the producer in a short term, 57 days.

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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

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Universidade Estadual de Campinas . Faculdade de Educação Física