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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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  • Document
FEM simulation by Ansys
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Data in summary: 1- Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment 2- Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe. 3- Half hourly building demand and deferrable load breakdowns: This is processed data that includes building total and HH instances of deferrable loads for all sub-categories of loads considered in this work. It also includes HH instances of PV generation, and outside air temperature. 4- Early morning ramp rates following plant start-up: This is a file containing the difference between two instantaneous recordings of total building electricity consumption that shows the continuous fluctuation in total electricity demand in the building. 5- CO2-raw (Typical office): This files contains actual CO2 data in an office that represents typical space occupant density in the case study building. 6- CO2-raw (worst case): This files contains actual CO2 data in a teaching space that represents the highest observed space occupant density in the case study building. 7- Warming and cooling rates in the worst case zones: This file include actual data describing the operational temperature in the worst case zones most prone to overheating in summer and excessive heat loss in winter.
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https://doi.org/10.1016/j.jaad.2019.11.023 Supplemental materials for this retrospective case series including all (n=38) pemphigoid patients at the University of Pennsylvania followed at least 1 year after RTX or until death. Outcomes followed consensus definitions. The primary endpoint was complete remission (CR). Secondary endpoints were CR off therapy (CROT), corticosteroid dose, relapse, serious adverse events (SAEs), and autoantibody titers.
Data Types:
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This data set corresponds to the experimental data reported in the manuscript "E-DATA: a comprehensive field campaign to investigate evaporation enhanced by advection in the hyper-arid Altiplano" by Francisco Suárez, Felipe Lobos, Alberto de la Fuente, Jordi Vilà-Guerau de Arellano, Ana Prieto, Carolina Meruane and Oscar Hartogensis. The data are in matlab (*.mat) or ascii files (*.dat or *-csv). Each file has a description of the data (variables, units, etc.)
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The dataset includes 2,016 impact echo signals from eight identical laboratory-made concrete specimens. This dataset is annotated in two classes: sound concrete (Class S) and defected concrete (Class D).
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This file presents data of the derivative of thermal conductivity with respect to temperature.
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  • Document
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.
Data Types:
  • Dataset
  • Document
Transcript of the interviews with the participants.
Data Types:
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  • Document
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.
Data Types:
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  • Tabular Data
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  • Document