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34 results for “CCM”
GEOS CCM free-running simulation data of the Pacific-Northwest pyrocumulonimbus Event-like aerosol injection, SWIRL selection
<p>Data from GEOS CCM free running simulation of the Pacific-Northwest pyrocumulonimbus Event. </p> <p>The simulations have been designed and performed by Sampa Das and Peter R. Colarco at the NASA Center for Climate Simulations; the computing resources supporting the simulations shown in this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center.</p> <p>The datasets stored here have been generated by Giorgio Doglioni starting from the whole results of the simulations.</p> <p>The files are in hdf5 format and their structure and content is described in the README file. </p> <p> </p>
Simulations of cosmogenic isotopes production via CCM SOCOL-AERv2-BE
<p>Simulations of cosmogenic isotopes production via CCM SOCOL-AERv2-BE.</p> <p>Production for 10Be, 7Be, 36Cl and 14C.</p>
Arrythmia Burden in Cardiac Contractility Modulation (CCM)
ClinicalTrials.gov study NCT05704426. IPD Sharing: NO. Countries: 1. Publications: 7.
Simulations of 10Be production via CCM SOCOL-AERv2-BE (96 tracers for 10Be)
<p>Here you can see global 10Be content (at/m^3) of different sources (see tab. 1 in paper) produced by GCR and SCR for the ground level. </p> <p>Here are two folders with the same structure:<br> 1) GCR<br> 2) SCR</p> <p>In thise folders 96 .txt files and 97 .xls files.</p> <p>In .txt files the first column is latitude and the first line is longitude. For each point values of 10Be content (at/m^3) in ground level.</p> <p>In .xls the same values, but grid for these values in an independent file called grid_size.xls.</p> <p>You can choose a format file which more comfortable for you.</p>
The installation-level China coal model IL-CCM
<p><strong>The installation-level China coal model IL-CCM</strong><br> The full version of a China coal transport model with a very high spatial resolution.</p> <p><strong>What it does</strong><br> The code works in a few steps:<br> 1. Take easily understandable and readable xlsx input files on networks, plants, demand etc, and create project build files form this (done in R).<br> 2. Take the build files and create an LP problem file from it (in python, either locally or on AWS Sagemaker).<br> 3. Solve the problem from the LP problem file, and write solution to a txt file (either in Cplex interactive or in python).<br> 4. Process the solution txt file and write to easily understandable and readable xlsx (in R).<br> The packages required to run these scripts are included in the environment.yml (for python) and the renv.lock file (for R; this first requires installation of renv package: https://rstudio.github.io/renv/articles/renv.html; after installation run renv:restore()).</p> <p><strong>The model</strong><br> The model optimizes for a minimum cost of production + transport + transmission. <br> Production meaning coal mining costs, transport meaning rail/truck/riverborne/ocean-going transport and handling costs, and transmission meaning inter-provincial transport of electricity via UHV cables.</p> <p><strong>Constraints in the optimization</strong><br> The constraints in the mini testbench are the same as in the full model. These are:<br> - Mines (or any other node) cannot supply types of coal they do not produce.<br> - The flow of coal of each type out of a node cannot exceed flows of coal of each type into a node plus supply by the node (with supply being non-zero only for mines).<br> - The energy content of the supply and the flows of coal of each type into a node have to be at least equal to the demand for electricity, plus other thermal coal demand, plus the energy content of flows of coal of each type out of a node. Note that only mines can supply coal, all demand for electrical power occurs in provincial demand nodes, and demand for other thermal coal is placed at city-level nodes.<br> - The amount of hard coking coal (HCC) flowing into a node has to at least be equal to the steel demand multiplied by 0.581. Note that all steel demand is placed in provincial level steel demand nodes, which are connected with uni-directional links from steel plants to steel demand nodes. This means no coal can flow out of a steel demand node and we do not need further formulae for mass balances. Also note that we presume a mix of coking coal need to produce a ton of steel of 581 kg Hard coking coal (HCC), 176 kg of soft coking coal (SCC), and 179 kg of pulverized coal for injection (PCI).<br> - The amount of soft coking coal (SCC) flowing into a node has to at least be equal to the amount of HCC flowing into that node, multiplied with 0.581/0.176.<br> - The amount of pulverized coal for injection (PCI) flowing into a node has to at least be equal to the amount of HCC flowing into that node, multiplied with 0.581/0.179.<br> - The total volume of all coal types transported along a link cannot exceed the transport capacity of that link. Note that this constraint is applied only to those links with a non-infinite transport capacity. In practice this means rail links are assumed to have a transport capacity, ocean routes, rivers, and road links are assumed to have infinite capacity.<br> - The total amount of energy transported along a link cannot exceed the transmission capacity of that link. That is, the amount of each coal type multiplied with the energy content of each coal type cannot exceed the electrical transmission capacity of links. This constraint is applied only links between power plant units and provincial electricity demand nodes, as well as UHV transmission links between provincial electricity demand nodes. These are the only links along which electrical energy is transported. All other links transport physical quantities of coal. This line simultaneously deals with the production capacity (MW) and conversion efficiency of power plants: the energy transported over a link cannot exceed the volume of each coal type, multiplied with the energy content of each coal type, multiplied with the energy conversion factor of the link. For links between coal fired power plant units and provincial electricity demand nodes, this is equal to the conversion effincy of the power plant unit. For UHV transmission links between two provincial level electricity demand nodes, this is equal to (1- transmission losses) over that UHV line, with transmission losses calculated based on transmission distance and a benchmark loss for UHV-DC or UHV-AC lines.<br> - The handling capacity of ports cannot be exceeded. Specifically, the total amount of coal flowing out of a port cannot exceed its handling capacity.<br> - The production capacity of steel plants cannot be exceeded. Specifically, the total amount of hard coking coal, soft coking coal, and pulverized coal for injection flowing out of a steel plant node (and towards a provincial steel demand node) cannot exceed the steel plant's production capacity multiplied by 0.581+0.176+0.179, the mix of different coking coals needed to produce steel.</p> <p><strong>Technical notes</strong><br> - All transport costs are pre-calculated for each link, and include a fixed handling costs and a distance based transport cost, based on the type handling (origin and destination) and type of transport (separate for rail, truck, riverborne, ocean-going. A small number of coal rail lines has specific handling and transport costs).<br> - Some of the capacities are already reported in the input sheet for the edges. The physical transport capacity from this sheet is used. For capacities of ports, steel plants, and electrical transmission capacities, the data from the separate port/steel plant/electrical capacities sheets is used.<br> - An example lp file is included to make this repository as self-contained as possible. This lp file is zipped to stay within github file size limits.</p> <p><strong>Contributions</strong><br> This model was developed by Jorrit Gosens and Alex Turnbull. Frank Jotzo was part of the team that wrote the publication introducing this model.</p> <p><strong>License</strong><br> MIT License as separately included. <br> In short, do what you want with this script, but refer to the original authors when you use or develop this code.</p>
Treat_CCM: Propranolol in Familial Cerebral Cavernous Malformation
ClinicalTrials.gov study NCT03589014. IPD Sharing: YES. Countries: 1. Publications: 11.
Behavioral Health Interdisciplinary Program - Collaborative Chronic Care Model (BHIP-CCM) Enhancement Project 2.0
ClinicalTrials.gov study NCT05997836. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of CCM in HF With Higher Ejection Fraction
ClinicalTrials.gov study NCT05064709. IPD Sharing: NO. Countries: 1. Publications: 4.
CCM Italian Registry
ClinicalTrials.gov study NCT04327323. IPD Sharing: NO. Countries: 1. Publications: 8.
The Predictive Value of Dobutamine Echo-stress in the Clinical Response to CCM Therapy in Advanced HF
ClinicalTrials.gov study NCT06973902. IPD Sharing: NO. Countries: 1. Publications: 16.
Natural History of Familial Cerebral Cavernous Malformations: the CCM_Italia Cohort Study
ClinicalTrials.gov study NCT06983132. IPD Sharing: NO. Countries: 1. Publications: 6.
CCM in Heart Failure With Preserved Ejection Fraction
ClinicalTrials.gov study NCT03240237. IPD Sharing: UNDECIDED. Countries: 8. Publications: 2.
Proactive Community Case Management (Pro-CCM) in Rural Madagascar
ClinicalTrials.gov study NCT05223933. IPD Sharing: YES. Countries: 1. Publications: 1.
Simulations of 7Be via CCM SOCOL-AERv2-BEv1
<p>Simulations of 7Be via CCM SOCOL-AERv2-BEv1 with nudging for 2002-2008</p>
Results of the CCM SOCOLv3 runs with and without orographic gravity waves (OGW) parameterizations
<p>1. Model run without any OGW parameterization:</p> <p>T_no_OGW.nc – Temperature (K)</p> <p>U_no_OGW.nc – Eastward zonal wind (m/s)</p> <p> </p> <p>2. Model run with the OGW parameterization of Lott and Miller:</p> <p>T_Lott_Miller.nc – Temperature (K)</p> <p>U_Lott_Miller.nc – Eastward zonal wind (m/s)</p> <p> </p> <p>3. Model run with the OGW parameterization of the Saint-Petersburg University (SPBU): </p> <p>T_SPBU.nc – Temperature (K)</p> <p>U_SPBU.nc – Eastward zonal wind (m/s)</p> <p>-------------------------------------------------------------------------------------------------</p> <p> (monthly and zonal mean values averaged over 2009-2018 time period) </p>
mTORC1 signaling in brain endothelial progenitors contributes to CCM pathogenesis in a Pdcd10-deficient mouse model
GEO Series GSE246598. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
CCM and Implementation of Guideline-Directed Medical Therapy in Patients With Heart Failure (Fix-GDMT-HF)
ClinicalTrials.gov study NCT06580392. IPD Sharing: NO. Countries: 1. Publications: 0.
Subanalysis in Patients With CARDIoLAMinopathy Enrolled to REPORT-CCM Registry
ClinicalTrials.gov study NCT04904393. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CCM OPEN HF Registry
ClinicalTrials.gov study NCT07112924. IPD Sharing: NO. Countries: 1. Publications: 0.
Optimizer System With ODOCOR II CCM™ Leads
ClinicalTrials.gov study NCT04755569. IPD Sharing: NO. Countries: 3. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.