21 resultados para likelihood to publication


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Leaf analysis is the chemical evaluation of the nutritional status where the nutrient concentrations found in the tissue reflect the nutritional status of the plants. Thus, a correct interpretation of the results of leaf analysis is fundamental for an effective use of this tool. The purpose of this study was to propose and compare the method of Fertilization Response Likelihood (FRL) for interpretation of leaf analysis with that of the Diagnosis and Recommendation Integrated System (DRIS). The database consisted of 157 analyses of the N, P, K, Ca, Mg, S, Cu, Fe, Mn, Zn, and B concentrations in coffee leaves, which were divided into two groups: low yield (< 30 bags ha-1) and high yield (> 30 bags ha-1). The DRIS indices were calculated using the method proposed by Jones (1981). The fertilization response likelihood was computed based on the approximation of normal distribution. It was found that the Fertilization Response Likelihood (FRL) allowed an evaluation of the nutritional status of coffee trees, coinciding with the DRIS-based diagnoses in 84.96 % of the crops.

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Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

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A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

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The meeting of the Publication "Evidence Based Telemedicine - Trauma and Emergency Surgery" (TBE-CiTE), through literature review, selected three recent articles on the treatment of victims stab wounds to the abdominal wall. The first study looked at the role of computed tomography (CT) in the treatment of patients with stab wounds to the abdominal wall. The second examined the use of laparoscopy over serial physical examinations to evaluate patients in need of laparotomy. The third did a review of surgical exploration of the abdominal wound, use of diagnostic peritoneal lavage and CT for the early identification of significant lesions and the best time for intervention. There was consensus to laparotomy in the presence of hemodynamic instability or signs of peritonitis, or evisceration. The wound should be explored under local anesthesia and if there is no injury to the aponeurosis the patient can be discharged. In the presence of penetration into the abdominal cavity, serial abdominal examinations are safe without CT. Laparoscopy is well indicated when there is doubt about any intracavitary lesion, in centers experienced in this method.

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Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

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Nine Brazilian scientists with an outstanding profile of international publications were invited to publish an original article in the same issue of a Brazilian Journal (Anais da Academia Brasileira de Ciências). The objective was to measure the impact of the papers on the number of citations to the articles, the assumption being that these authors would carry their international prestige to the Brazilian periodical. In a 2-year period there was a larger number of citations of these articles compared to others published in the same journal. Nevertheless, the number of citations in Brazilian journals did not equal the number of citations obtained by the other papers by the same authors in their international publications within the same 2-year period. The reasons for this difference in the number of citations could be either that less significant invited articles were submitted or that it was due to the intrinsic lack of visibility of the Brazilian journals, but this could not be fully determined with the present data. Also relevant was a comparison between the citations of Brazilian journals and the publication in Brazilian journals by these selected authors. A clear imbalance due to a remarkable under-citation of Brazilian authors by authors publishing in Brazilian journals raises the possibility that psychological factors may affect the decision of citing Brazilian journals.