Contributors:Callahan Owen, Eichhubl Peter, Davatzes Nicholas
Files and tables in support of the manuscript “Mineral precipitation as a mechanism of fault core growth” submitted to the Journal of Structural Geology. Table S1 contains structural measurements from Dixie Comstock, Nevada, USA. Map S1 is a .kmz file that can be downloaded and opened with Google Earth that includes a geologic map of the Dixie Comstock area, approximate locations for several other figures from the submitted text, sample locations, and scanline locations presented in Table S2. Unedited versions of all photographs used in the figures are also included.
Background. Whilst cannabis commercialization is occurring rapidly guided by highly individualistic public narratives, evidence that all congenital anomalies (CA) increase alongside cannabis use in Canada, a link with 21 CA’s in Hawaii, and rising CA’s in Colorado indicate that transgenerational effects can be significant and impact public health. It was therefore important to study Northern New South Wales (NNSW) a known cannabis use centre.
Methods. Design: Cohort. 2008-2015. Setting: NNSW and Queensland (QLD), Australia. Participants. Whole populations. Exposures. Tobacco, Risky Alcohol, Annual cannabis. Source: National Drug Strategy Household Surveys 2010, 2013. Main Outcomes. CA Rates. NNSW-QLD comparisons. Geospatial and causal regression.
Results. Cardiovascular, respiratory and gastrointestinal anomalies rose with falling tobacco and alcohol but rising cannabis use rates across Queensland. Maternal age NNSW-QLD was not different (2008-2015: 4,265/22,084 v. 96,473/490,514 >35 years, Chi.Sq.=1.687, P=0.194). A higher rate of NNSW cannabis-related than cannabis-unrelated defects occurred (prevalence ratio (PR)=2.13, 95%C.I. 1.80-2.52, P=3.24x10-19). CA’s rose more potently with rising cannabis than with rising tobacco or alcohol use. Exomphalos and gastroschisis had the highest NNSW:QLD PR (6.29(2.94-13.48) and 5.85(3.54-9.67)) and attributable fraction in the exposed (84.11%(65.95-92.58%) and 82.91%(71.75-89.66%), P=2.83x10-8 and P=5.62x10-15). In multivariable geospatial models cannabis was significantly linked with cardiovascular (atrial septal defect, ventricular septal defect, tetralogy of Fallot, patent ductus arteriosus), genetic (chromosomal defects, Downs syndrome), gastrointestinal (small intestinal atresia), body wall (gastroschisis, diaphragmatic hernia) and other (hypospadias) (AVTPCDSGDH) CA’s. In linear modelling cannabis use was significantly linked with anal stenosis, congenital hydrocephalus and Turner syndrome (ACT) and was significantly linked in borderline significant models (model P1.3 ranging up to 3.8x1030 making uncontrolled confounding unlikley.
Conclusions. These results suggest that population level CA’s react more strongly to small rises in cannabis use than tobacco or alcohol; cardiovascular, chromosomal, body wall and gastrointestinal CA’s rise significantly with small increases in cannabis use; and that cannabis is a bivariate correlate of AVTPCDSGDH and ACT anomalies and is robust to adjustment for other substances.
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.
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.
Contributors:Waldron Alexander, Pecci Filippo, Stoianov Ivan
This dataset is supplementary data for: Waldron, A., Pecci, F., Stoianov, I. (2020). Regularization of an Inverse Problem for Parameter Estimation in Water Distribution Networks. Journal of Water Resources and Planning Management, 146(9):04020076 (https://doi.org/10.1061/(ASCE)WR.1943-5452.0001273).
The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence. Any use of this dataset must credit the authors by citing the above paper.
BWFLnet is an operational network in Bristol, UK, operated by Bristol Water in collaboration with the InfraSense Labs at Imperial College London and Cla-Val Ltd.
The data provided is a the product of a long term research partnership between Bristol Water, Infrasense Labs at Imperial College London and Cla-Val on the design and control of dynamically adaptive networks.
We acknowledge the financial support of EPSRC (EP/P004229/1, Dynamically Adaptive and Resilient Water Supply Networks for a Sustainable Future).
All data provided is recorded hydraulic data with locations and names anonymised. The authors hope that the publication of this dataset will facilitate the reproducibility of research in hydraulic model calibration as well as broader research in the water distribution sector.
A low-resolution WRF model has run which has exact same spatial resolution with GFS of NCEP which is initial and boundary conditions data and extracted data from the four grid points of this model which surround the related wind farm are used as predictor variables to the gradient boosting machines. Thus; the downscaling process is basically done by the coupling of the low-resolution numerical weather prediction model and machine learning algorithm. The results of this hybrid model are also compared with the results of a WRF model which has a higher spatial resolution in terms of both statistical measures and required time. As a result of this; the study claims a novel approach for not only increasing the accuracy of the wind power generation forecasts but also reducing the computational expense.
Since wind energy increases its share in installed energy capacity thanks to its maturity in terms of technology and decreasing costs; wind power generation forecasts with high performance becomes very crucial to have not only the security of the electricity supply of the countries all around the world but also prevent energy imbalance penalties for the wind farm owners. Besides, this study shows that a better performance than the downscaling processes of the numerical weather prediction models do can be achieved in shorter periods with machine learning algorithms. Thus; this study also highlights that the coupling of machine learning algorithms with the numerical weather prediction models can play an effective role in the future in terms of atmospheric sciences. Consequently; this work provides basically not only increasing the accuracy of the wind power generation forecasts when it is compared with the numerical weather prediction models but also decreasing the computational expense by doing the downscaling process of numerical weather prediction models with gradient boosting machines.
This repository comprehends all the data supporting the analysis of the recent volcano-tectonic activity of the Ririba rift, at the southern tip of the Ethiopian Rift Valley, near the Kenya/Ethiopia border. It consists of a pdf file, two .kmz files and two Excel tables.
Specifically, the pdf file ('Supplementary material') includes a list of the samples collected during fieldwork (Table S1), details concerning the methodology employed in the morphometric analysis of volcanic structures (S2) and in the statistical analysis of vent clustering and its results (S3), and chemical analysis of the collected volcanic rock samples (Table S4).
The two Excel tables report the data used for the morphometric analysis of the subset of volcanic centres (specifically, 26 for Dilo VF and 41 for Mega VF) which could be well delimited from satellite images (Table S2d contains information of the volcanic cones and lava flows, while Table S2e regards maars and tuff rings).
The data collected from remote sensing analysis of the two volcanic fields it is also reported as two .kmz files ("Dilo VF" and "Mega VF"). In each volcanic field, the subset of volcanic centres which have been used to extract morphometric measurements, lava flows, characteristic alignment and vent elongation trends and the sampling sites of the collected rocks are marked.