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This database comes from the archives of the surgery department of the Hospital of the university of state of Haiti (HUEH). The ethics laboratory (LABMES) of the faculty of medicine and pharmacy of the university of state of Haiti via the vice decanate had given me approval, after acceptance of the protocol of my work on peritonitis, to take the data archives of the surgery department from January 2013 to December 2018. Of the 140 files for which the diagnosis of peritonitis was made and operated in the departments, 126 files were retained for the work and of these 126 the 91 files which constitute the Sample is the subject of this database, which must be kept confidential and should not be used without the author's permission.
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Detected major oxides in Lokpanta shale using x-ray fluorescence.
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This file presents data of the derivative of thermal conductivity with respect to temperature.
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This file is the appendix of article "Portfolio Management under Multiple Regimes: Strategies that Outperform the Market" from Lewin and Campani (2020). Here, we present in Portuguese the mathematical procedures to set up the applied model following Campani, Garcia and Lewin (2020). This information allows the researcher to reproduce the model. Our research objective is to open field for a broader application of regime swithing models in asset allocation worldwide.
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Transcript of the interviews with the participants.
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Using the PiKh–model [1], a test data set for training the neural network is formed. The data for training is presented in the file (raw_data_table.csv). The architecture of the neural network can be arbitrary and is set by the settings file (experiment_plan.json). To build the architecture of a neural network, it is necessary to determine the names of the input nodes, the names of the output nodes and set the parameters for hidden layers and the output layer. Each output layer is characterized by a name and parameters that determine the number of nodes, the type of activation function, the optimization algorithm, and the method for distributing errors between nodes. The settings file allows you to set the number of epochs during the training of the neural network, the interval between epochs when the learning results are saved (the interval of data recording on the hard disk), the error value (MSE), and the value of the task stop time for cooling the processor. The values of the output streams for the output sections m=7.8 are presented in the file (epoch0000300000_R.xlsx) under the column names (7.outputA), (8.outputA). The values (7.outputA), (8.outputA) are defined for each row of the test data set for training the neural network.
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The objective of this dataset was to present the forage biomass production over time in different pasture management systems. We selected two farms located in the Western region of São Paulo State, Brazil. Pasture field data collection was carried out in two farms during three dates (June and November 2018 and March 2019) over two seasons (wet and dry). Samples were regularly taken through time to monitor forage biomass. These fields represent a wide variety of pasture management, as follow: Farm 1 (Santa Clara): i) traditional, low forage productivity, cattle rotation; ii) traditional, intermediate forage productivity, fertilized, cattle rotation; iii) intensified pasture, high forage productivity, reformed, cattle rotation. Farm 2 (Poderosa): i) traditional degraded*, recently reformed with millet + grass, cattle rotation; ii) traditional, low forage productivity, signs of degradation, fertilized, cattle rotation. *degraded was based on visual analysis of pasture area with sparse grass and exposed soil in some areas. With the support of NDVI images from the MODIS sensor, sample pixels were used to allocate the sample points. The areas of these pixels were divided into nine sampling points and in each of these points, the forage biomass was collected. Soil analyses were also carried out in two seasons (June 2018 and March 2019). The data files were organized in three folders. Each folder represents one field campaign. These folders have a shapefile of all the fields, the same file in kml extension (to open on Google Earth) and a zip file with photography of each field during the field campaign. The attribute table of the shapefile has a description of the fields and biomass. Excel files show the same information of the attribute table and a description of the items. A figure with the template of the biomass collection scheme is also available. Soil analyses are in the folders 'June 2018' and 'March 2019'. A more detailed description and discussion about these data and their association with soil chemical analysis were described in a scientific report (available by request). The biomass collection allowed the analysis of the forage production and better diagnoses about resource utilization strategies over the different pasture systems. This work was funded by the São Paulo Research Foundation (process numbers 2018/10770-1, 2017/06037-4, 2016/08741-8, 2017/08970-0, 2018/11052-5 and 2014/26767-9) as part of the Global Sustainable Bioenergy Initiative.
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Zoological Studies
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Thermal neutron interactions parameter of nuclear reactions with UHTCs.
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Subclonal Mutation Selection in Mouse Lymphomagenesis Identifies Known Cancer Loci And Suggests Novel Candidates. - Supplementary figures and data
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  • Tabular Data
  • Dataset
  • Document
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