1000 resultados para índices fitossociológicos
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The swine breeder rearing environment directly affects the animal's performance. This research had the objective of developing a thermal, aerial and acoustic environmental evaluation pattern for boar housing. The experiment was carried on a commercial swine farm in Salto County -SP, Brazil. Thermal, aerial and acoustic environment data of rearing conditions were registered. Data were statistically analyzed using as threshold the ideal housing environment that leads to animal welfare. Results showed that ambient temperature was around 70% beyond normal range, while air relative humidity, air speed and gases concentration were within threshold values. Noise level data besides being within normal range did not present large variation. In relation to the fuzzy logic analysis it was possible to build up a scenario which indicated that the best welfare indexes to male swine breeders happens when thermal comfort index are close to 80%, and noise level is lower than 40 dB. In the other hand the worst welfare index occur in the sector where the thermal comfort values are below 40% at the same time that the noise level is higher than 80 dB leading to inadequate conditions to the animal, and may directly interfere in the reproduction system performance.
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Remote sensing data are each time more available and can be used to monitor the vegetal development of main agricultural crops, such as the Arabic coffee in Brazil, since that the relationship between spectral and agronomical data be well known. Therefore, this work had the main objective to assess the use of Quickbird satellite images to estimate biophysical parameters of coffee crop. Test area was composed by 25 coffee fields located between the cities of Ribeirão Corrente, Franca and Cristais Paulista (SP), Brazil, and the biophysical parameters used were row and between plants spacing, plant height, LAI, canopy diameter, percentage of vegetation cover, roughness and biomass. Spectral data were the reflectance of four bands of QUICKBIRD and values of four vegetations indexes (NDVI, GVI, SAVI and RVI) based on the same satellite. All these data were analyzed using linear and nonlinear regression methods to generate estimation models of biophysical parameters. The use of regression models based on nonlinear equations was more appropriate to estimate parameters such as the LAI and the percentage of biomass, important to indicate the productivity of coffee crop.
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The main objective of this work was to evaluate the linear regression between spectral response and soybean yield in regional scale. In this study were monitored 36 municipalities from the west region of the states of Parana using five images of Landsat 5/TM during 2004/05 season. The spectral response was converted in physical values, apparent and surface reflectances, by radiometric transformation and atmospheric corrections and both used to calculate NDVI and GVI vegetation indices. Those ones were compared by multiple and simple regression with government official yield values (IBGE). Diagnostic processing method to identify influents values or collinearity was applied to the data too. The results showed that the mean surface reflectance value from all images was more correlated with yield than individual dates. Further, the multiple regressions using all dates and both vegetation indices gave better results than simple regression.
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Lianas are characteristic, abundant and ecologically important members of tropical forest but they have been neglected in floristics and phytossociological studies. This work presents a floristic survey of the lianas species at Estação Ecológica do Noroeste Paulista (EENP), and a comparison of the list of species recorded in this work with those reported for other fragments of São Paulo state. The EENP (20º48'36'' S and 49º22'50'' W) is at 468 m of altitude and comprises an area of 168,43 ha, divided into three fragments of vegetation. Samples of lianas were collected in the interior and along the edges of the forest fragments. It was identified 105 species: 99 Magnoliopsida (60 genera and 22 families); six Liliopsida (three genera and three families). The richest families in species comprised 59% of the total of lianas sampled. The dendrogram of similarity showed a low similarity between the forest situated in the littoral (Atlantic Forest) and those located in the interior of the state of São Paulo. Some other authors, also analysing the similarity of forest of the interior and Atlantic Forest of São Paulo state, but considering only the trees reported similar result.
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The purpose of the present study was to verify the factorial validity of a learning strategy scale as well as to explore the concurrent validity of the instrument in regard to students´ academic achievement. The sample was composed of 815 basic education children from both public and private schools of São Paulo and Minas Gerais. The Learning Strategy Scale was collectively applied. Exploratory factorial analyses were conducted to achieve the purposes of the study. The alphas of Cronbach of the instrument and of its three subscales showed good reliability. Variance analyses showed significant differences between school achievement and punctuation in the scale. The data were discussed in terms of their possible implications for the psycho-educational evaluation area.
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The aim of this paper was to identify, according to gender, the indexes of Mental Health and the Psychosocial Risk Factors in workers at a state University. A sample of 400 was randomly selected, 253 female and 147 male. They were assessed by means of The Questionnaire SWS Survey (Self, Work and Social) (Ostermann & Gutiérrez, 1992), validated in Brazil by Guimarães and Macfadden (1999). Univariate, bivariate, multivariate statistics were assessed. Significant associations emerged from Mental Health and Gender, and Psychosocial Risk Factors and Gender. Women showed greater Psychosocial Risk Factors, Work and Social Stress, worst mental health than men (p < 0.05), which place them at greater risk for developing physical and/or mental illness.
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Thyroid nodules are frequent findings, especially when sensitive imaging methods are used. Although thyroid cancer is relatively rare, its incidence is increasing, particularly in terms of small tumors, which have an uncertain clinical relevance. Most patients with differentiated thyroid cancer exhibit satisfactory clinical outcomes when treatment is appropriate, and their mortality rate is similar to that of the overall population. However, relapse occurs in a considerable fraction of these patients, and some patients stop responding to conventional treatment and eventually die from their disease. Therefore, the challenge is how to identify the individuals who require more aggressive disease management while sparing the majority of patients from unnecessary treatments and procedures. We have updated the Brazilian Consensus that was published in 2007, emphasizing the diagnostic and therapeutic advances that the participants, representing several Brazilian university centers, consider most relevant in clinical practice. The formulation of the present guidelines was based on the participants' experience and a review of the relevant literature.
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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
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Universidade Estadual de Campinas. Faculdade de Educação Física
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Universidade Estadual de Campinas. Faculdade de Educação Física
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Univesidade Estadual de Campinas. Faculdade de Educação Física
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Universidade Estadual de Campinas. Faculdade de Educação Física
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Universidade Estadual de Campinas. Faculdade de Educação Física