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RRDE results from KOH activated char
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This file contains SPSS outputs involving descriptive analysis, tests of assumptions, and independent t-test analysis.
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The traffic conditions in developing nations of Asia are highly heterogeneous due to the presence of various vehicle types. The objectives of this paper are as follows. To study the road traffic capacity and delays at urban merging sections consisting of the mixed traffic stream. To analyse the interactions of vehicles both laterally and longitudinally by the inclusion of vehicle-type dependent factor. Using the data collected at a five-lane urban merging section using video recording method for the evaluation. To study the relation between macroscopic parameters (speed, flow, density, occupancy) and microscopic parameters (lateral clearance, average gap, space headway, lateral movement duration) is established by considering vehicle-type dependent factor. To analyse the effect of overtaking characteristics of different vehicle types at merge sections under mixed traffic conditions. The findings from this present research help in the operational analysis of merging locations on high-speed urban roads in Malaysia.
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Investigación política de hechos que marcaron Bolivia de 2008 a 2019
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supplementary tables and figures
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This is a survey data on 164 professionals that includes university faculty, senior administrative, technical, and managerial staff of an institution f higher learning. The question is to what extent do these professionals know about and used GIS/RS in their routine and research work? The hypotheses are (1) those who were aware and know GIS/RS were more likely to use the tool and this varies by background factors, (2) respondents who know about the tools were more likely to use them. Variables examined include background factors (including residence, age, marital status), education, and years of work experience. Other key variables examined were awareness, knowledge, attitudes and application and the tools. Findings showed that awareness about GIS/RS was significantly related to residence, age, marital status, education, type of carrier or profession. Knowledge was significantly associated with sources heard about GIS/RS. Also, obstacles to use was significantly related to residence, age, type of carrier or profession, and sources heard about GIS/RS. Finding of this data will be useful in driving increased knowledge and use of GIS/RS for planning and research all over the world.
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Structural analysis of mass transport deposits
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Raw uncropped original images that were used for making figures in Basu et al., PNAS 2020
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Supplemental Materials for JAAD Research Letter "Access to Mohs Surgery through the Choice program of the United States Department of Veterans Affairs "
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There are four main folders in the project: code, data, models and logdir. Data This folder contains all the data used from the two studied locations: Loc.1 (latitude=40.4º, longitude=6.0º) and Loc.2 (latitude=39.99º, longitude=-0.06º). Sorted by year, month and day, each location has three kinds of data: • The files named as just a number are 151x151 irradiance estimates matrices centered in the same location obtained from http://msgcpp.knmi.nl. The spatial resolution is 0.03º for both latitude and longitude. • The files named Real_ are the irradiance measurements at the location • The files named CopernicusClear_ are the clear sky estimates from the CAMS McClear model Each file contains the 96 15-minute samples for the same day in Matlab format and UTC time. Code All the python scripts used to train the neural networks and perform the forecasts. The main files are: • tf1.yml: List of the modules and versions used. A clean Anaconda environment created from this file can run all the code in the project. • learnRadiation.py: The script to train a new model. Changing the variables “paper_model_name” and “location”. The first variable selects the kind of model to fit and the second one the training location. • predictOnly.py: Loads a trained model and performs the forecast. Notice that the model and location must match the ones used to train the model stored in the “training_path” folder Models This folder contains all the trained models and their forecasting results. There is also a training folder to contain the last trained model. Logdir This folder stores Tensorboard files during training How to train and test a model A new model can be trained using “learnRadiation.py”. This script has three parameters • location: Selects the location where the model will be trained (LOC1 or LOC2) • paper_model_name: This sets the inputs to match the ones used in the models from the article. • training_path: The folder to save the trained model Then the “predictOnly.py” script allows performing the forecasts. It is important to set the same parameters as in the “learnRadiation.py” script. This program will generate the predictions and save them in the model folder. It also plots some days, which can be modified at the bottom of the script. For instance for LOC2 and model TOA & all real we would run: "python learnRadiation.py TOAallreal LOC2 training" This will train the neural network and save the results in the folder models/training. After this, we would generate the results and plot some days using: “python predictOnly.py TOAallreal LOC2 training” This will save the forecasts and real values in the training folder and show figures with 1 to 6 hour forecasts The models used for the article can also be evaluated by using predictOnly.py and targeting their folders. For instance, to evaluate the TOA & all real model used in the article, this command must be used: “python predictOnly.py TOAallreal LOC2 RtoaAllReal”
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