The autocalibration tool coupling SWMM with the Genetic Optimization Algorithm is used to constantly change the parameter combination of the model, and choose the best parameter group by comparing the performance of the model. The validated SWMM model is used to simulated hydrological processes with different rainfalls. the climate projection data for three periods in four return periods are used to analyze the flooding process under different scenarios. The simulation result data including the total waterlogging volume and waterlogging duration is showed as the attribute data of the manholes
Reports of major limb defects after prenatal cannabis exposure (PCE) in animals and of human populations in Hawaii, Europe and Australia raise the question of whether the increasing use of cannabis in USA might be spatiotemporally associated with limb reduction rates (LRR) across USA. Geotemporospatial analysis conducted in R. LRR was significantly associated with cannabis use and THC potency and demonstrated prominent cannabis-use quintile effects. In final lagged geospatial models interactive terms including cannabinoids were highly significant and robust to adjustment. States in which cannabis was not legalized had a lower LRR (4.28 v 5.01 /10,000 live births, relative risk reduction = -0.15, (95%C.I. -0.25, -0.02), P=0.021). 37-63% of cases are estimated to not be born alive; their inclusion strengthened these associations. Causal inference studies using inverse probabilty-weighted robust regression and e-values supported causal epidemiological pathways. Findings apply to several cannabinoids, are consistent with pathophysiological and causal mechanisms, are exacerbated by cannabis legalization and demonstrate dose-related intergenerational sequaelae.
Contributors:Hou Jinjin, Zhang Yongyong, Xia Jun, Pan Xingyao, Yang Moyuan, Leng Guoyong, Dou Ming
The geographic data sources were the digital elevation model (DEM) with the resolution of 10 m, land use with the resolution of 10 m, catchment boundary and drainage network, which were gathered for subcatchment subdivision for urban stormwater model development.
Dataset general description:
• This dataset reports 4195 recurrent neural network models, their settings, and their generated prediction csv files, graphs, and metadata files, for predicting COVID-19's daily infections in Brazil by training on limited raw data (30 time-steps and 40 time-steps alternatives). The used code is developed by the author and located in the following online data repository link:
• Models, Graphs, and csv predictions files:
1. Deterministic mode (DM): includes 1194 generated models files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated model files (40 time-steps), and their generated 7301 graphs and 7301 predictions files.
2. Non-deterministic mode (NDM): includes 20 generated model files (30 time-steps), and their generated 53 graphs and 53 predictions files.
3. Technical validation mode (TVM): includes 1001 generated model files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 358 models, which are a sample of 1001 models. Also, 1 model in control group for India.
4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11.
• Settings and metadata for the above 3 categories:
1. Used settings in json files for reproducibility.
2. Metadata about training and prediction setup and accuracy in csv files.
Raw data source that was used to train the models:
• The used raw data for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University): https://github.com/CSSEGISandData/COVID-19
• The models were trained on these versions of the raw data:
1. Link till 2020-06-29 (accessed 2020-07-08):
2. Link till 2020-06-13 (accessed 2020-07-08):
This prediction Dataset is licensed under CC BY NC 3.0.
Notice and disclaimer:
1- This prediction Dataset is for scientific and research purposes only.
The present dataset includes data and figures related to the submitted paper " Modeling and pseudo-inversion of transient multicomponent electromagnetic logging while drilling measurements " currently under review in Journal of Petroleum Science & Engineering.
The National Wet Waste Inventory is a point spatial dataset containing >56,000 modeled sources of municipal wastewater sludge; confined animal manure; industrial, institutional, and commercial (IIC) food waste; and fats, oils, and greases (FOG). These wastes are considered priority organic feedstocks for waste-to-energy conversion. While the dataset is spatially explicit to enable geospatial analysis, the point locations and the
associated waste magnitudes are considered representative and are intended to be used for national scale resource assessment modeling and may not reflect current on the ground conditions at a given location.
The present dataset includes data and figures related to the submitted paper " Detection of formation boundary using transient multicomponent electromagnetic logging while drilling method " currently under review in Journal of Petroleum Science and Engineering.
The ground truth data used in this paper are obtained from three different areas to verify the effectiveness and robustness of the proposed methods. The detailed data information is described as follows:
1) The ground truth data from the Pecan Street dataset are collected from real households in the Muller project in Austin, TX, USA. Muller project funded by the U.S. Department of Energy and the U.S. National Science Foundation are located on the site of the Austin’s former municipal airport, close to central Austin.The selected homes in the project received monitoring equipment that captures electricity use on less than or equal to 1 min intervals for the whole home and 6 to 22 major appliances. Data over one year from August 2015 to July 2016 are analyzed, which contain the application-level and the whole-house energy consumption data. The corresponding 1-hour level temperature data are collected from the nearest Mueller weather station. We down-sample the energy consumption data to the 1-hour level to maintain consistency with the resolution of the temperature data. After data cleaning, customers without air conditioners or with missing readings are omitted, and the data of 119 residential customers are selected for accuracy analysis.
2) The ground truth data from smart home dataset are collected from real households in the Smart Home project in the Western Massachusetts, USA. The goal of this project is to optimize home energy consumption. The project involves several different types of dataset, including apartment dataset of 114 single-family, home dataset of 7 household and solar panel dataset, etc. However, the apartment dataset only contains the aggregated electrical data which can not be used to verify the accuracy of the load decomposition. Therefore, data over one year from January 2016 to December 2016 of home B and home G with individual ACLs monitor are selected for robustness analysis.
3) The ground truth data from low voltage distribution area are collected from low voltage distribution boxes in a developed city, Jiangsu province, China. Power and corresponding temperature data over one year from 2017 to 2018 are used for local DR programs. The dataset involves different distribution areas (i.e., different aggregated DR customers), including garment factory, hotel, rural neighborhoods, etc. However, the sub-meter data of all the ACLs are unavailable, thus it will only be used for aggregated DR potential analysis.