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42 results for “power generator”
Nafion membranes for power generation from physiologic ion gradients
<p>Dataset for the publication: "Nafion membranes for power generation from physiologic ion gradients"</p>
Consensus Zero Turbulence Power Curve Generation
<p>Model in Microsoft Excel to generate zero turbulence power curve from reference power curve</p>
The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies
<p>This is the dataset for the paper: The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies</p> <p> This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p>
Viet Nam Technology catalogue for power generation and storage. Input for power system modelling
<p>The first Viet Nam Technology Catalogue was published in 2019. This new version includes all the technologies from the 2019 version that have been reviewed and updated where necessary. A main focus of the update has been to add new subcategories of technologies (roof-top solar PV, floating offshore wind, low wind speed turbines, improved flexibility of coal fired plants and pollution prevention technologies for coal power) as well as completely new technology descriptions and data sheets (tidal power, wave power, carbon capture and storage, coal CFB boilers and industrial cogeneration).</p> <p>This publication is developed under the Danish-Vietnamese Energy Partnership.</p> <p>The technologies described in this catalogue cover both very mature technologies and emerging technologies, which<br> are expected to improve significantly over the coming decades, both with respect to performance and cost. This<br> implies that the cost and performance of some technologies may be estimated with a rather high level of certainty<br> whereas, in the case of other technologies, both cost and performance today and in the future is associated with a<br> high level of uncertainty. All technologies have been grouped within one of four categories of technological<br> development described in the section on research and development indicating their technological progress, their<br> future development perspectives and the uncertainty related to the projection of cost and performance data.</p> <p>The technologies in the catalogue include the power production unit and the connection to the grid. This means<br> that the boundary for both cost and performance data are the generation assets plus the infrastructure required to<br> deliver the energy to the main grid. For electricity, this is the nearest substation of the transmission grid. This<br> implies that a MW of electricity represents the net electricity delivered, i.e. the gross generation minus the auxiliary<br> electricity consumed at the plant. Hence, efficiencies are also net efficiencies.</p> <p>The text and data have been edited based on Vietnamese cases to represent local conditions. For the mid- and long-<br> term future (2030 and 2050) international references have been relied upon for most technologies since Vietnamese<br> data is expected to converge to these international values. In the short run differences may exist, especially for the<br> emerging technologies. Differences in the short run can be caused by e.g. current rules and regulations and level of<br> market maturity of the technology. Differences in both the short and long run can be caused by local physical<br> conditions, e.g. seabed material and offshore conditions can affect costs of offshore wind farms and wind speed can<br> affect the dimensioning of rotor vs. generator which can influence the cost, or domestic coal quality can affect<br> efficiency and variable cost of coal-fired plants as well.</p> <p>Land use is assessed but the cost of land is not included in the total cost assessment since this depends on local<br> conditions.</p>
Rainwater-driven microbial fuel cells for power generation in the remote areas' raw data
<p>The possibility of utilizing rainwater as a sustainable anolyte in an air-cathode microbial fuel cell is investigated in this study. The results indicate that the proposed microbial fuel cell can work within a wide temperature range (from 0 to 30 oC), and under aerobic or anaerobic conditions. However, the rainwater season has a distinct impact. Under anaerobic conditions, the summer rainwater achieves a promised open circuit potential of 553±2 mV without addition of nutrients at the ambient temperature, while addition of nutrients leads to increase the cell voltage to 763±3 and 588±2 mV at 30 oC and ambient temperature, respectively. The maximum open circuit potential for the winter rainwater (492±1.5 mV) is obtained when the reactor is exposed to the air (aerobic conditions) at ambient temperature. Furthermore, the winter rainwater microbial fuel cell generates a maximum power output of 7±0.1 mWm-2 at a corresponding current density value of 44±0.7 mAm-2 at 30 oC. While, at the ambient temperature, the maximum output power is obtained with the summer rainwater (7.2±0.1 mWm-2 at 26±0.5 mAm-2). Moreover, investigation of the bacterial diversity indicates that lactobacillus sp. is the dominant electroactive genus in the summer rainwater, while in the winter rainwater, Staphylococcus sp. is the main electroactive bacteria. The cyclic voltammetry analysis confirms that the electrons are delivered directly from the bacterial biofilm to the anode surface and without mediators. Overall, the study opens a new avenue for utilizing a novel sustainable type of microbial fuel cell derived by rainwater.</p>
Generation and emission data for main power plants in Germany (2015 - 2021)
<p>This dataset contains generation and emission data for German main power plants (2015 - 2021). The underlying code with additional analysis is available on <a href="https://github.com/INATECH-CIG/CO2_emissions_factors_DE">Github</a> (still under development).</p> <p><strong>"blocks.xlsx":</strong></p> <p>This file collects power plant information per unit from different data sources. The <a href="https://www.entsoe.eu/data/energy-identification-codes-eic/">EIC </a>is the identifier used by ENTSO-E for the per unit generation time series [1]. This identifier is matched with BNA-ID and MaStR-Nr used in the official power plant list published by the <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Bundesnetzagentur</a> [2]. Information about the plant (location, name, block name, electrical and heat power capacity, fuel type) has been collected from the power plant lists from <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Bundesnetzagentur </a>[2] and <a href="https://www.umweltbundesamt.de/dokument/datenbank-kraftwerke-in-deutschland">Umweltbundesamt</a> [3]. Fuel-specific emission factors have been assigned from <a href="https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0">data published by Umweltbundesamt</a> [4] according to plant type and location. The ETS ID is the identifier of the installation in the <a href="https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en">EU emissions trading system</a>. Note that EIC, BNA-ID and MaStR-Nr differ for each generation unit, whereas the ETS ID relates to a stationary installation which might contain several generation units.</p> <p><strong>"block-generation.xslx"</strong><br> This file contains yearly generation for each generation unit calculated from the per unit generation time series published by ENTSO-E. Generation units are identified by the EIC and the BNA-ID.</p> <p><strong>"installation-results-yearly"</strong></p> <p>This file contains yearly results for generation and emissions for each installation identified by the ETS-ID [5]. Each installation might contain several generation units. Power generation has been calculated from per unit generation time series (ENTSO-E) for the units associated with the installation. Total emissions represent verified emissions as published as part of the EU emission trading system. These total emissions have been split up into emissions associated with power generation and heat provisioning based on the free allocations granted to the installation, <span class="math-tex">\(E_i=E_i^{el}+E_i^{heat}\)</span>. From emissions associated with power generation and total generation, installation-specific emission factors have been calculated, whereas fuel-specific emission factors are used to estimate the utilization factor of the installation.</p> <p>Emissions associated with heat are calculated by <span class="math-tex">\(E_{i}^{heat}=H_i\cdot hb\)</span>, where <span class="math-tex">\(H_i\)</span> is the heat provided by the installation and <span class="math-tex">\(hb=62.3 tCO_2/TJ\)</span> is the heat benchmark used in the EU ETS as part of the process for determining <a href="https://climate.ec.europa.eu/system/files/2016-11/gd1_general_guidance_en.pdf">free allocations</a> [6,7,8]. We assume that free allocations are calculated from the heat as follows:</p> <p><span class="math-tex">\(A_i(k) = H_i\cdot hb\cdot\beta(k)\cdot[\sigma + (1-\sigma)\gamma(k)]\)</span></p> <p>Here <span class="math-tex">\(A_i(k)\)</span> denotes the free allocations in year <span class="math-tex">\(2013+k\)</span> and <span class="math-tex">\(\beta(k)=(1-k\cdot 0,0174)\)</span> is the linear reduction factor. The term <span class="math-tex">\(\gamma(k)=(0.8-(0.5/7)\cdot k)\)</span> represents the carbon leakage exposure factor for provisioning to installations exposed to no significant carbon leakage risk. This factor is <span class="math-tex">\(1\)</span> for heat provisioning to installations which show a significant risk for carbon leakage. The parameter <span class="math-tex">\(\sigma\)</span> denotes the share of heat provided to the latter sector (with risk of carbon leakage), whereas <span class="math-tex">\((1-\sigma)\)</span> gives the remaining share of heat provided to the sector without significant risk of carbon leakage. The parameter <span class="math-tex">\(\sigma\)</span> has been calculated by comparing the given data for free allocations for consecutive years <span class="math-tex">\(A_i(k+1)/A_i(k)\)</span> under the assumption that <span class="math-tex">\(H_i(k+1)=H_i(k)\)</span>. Values for <span class="math-tex">\(\sigma\)</span> are given in the file "<strong>sigma.xlsx</strong>". Note that no free allocations are given for heat provided to installations taking part in the EU ETS, so these emissions are not covered by this approach. Not for all installation consistent information for free allocations was available, in these cases no entry in the file "installation-results-yearly.xslx" is given, respectively.</p> <p>The utilization factor is calculated by <span class="math-tex">\(G_i/((E_i^{el}/e(f_i))\)</span>, where <span class="math-tex">\(G_i\)</span> denotes the power generation at location <span class="math-tex">\(i\)</span>, <span class="math-tex">\(E_i^{el}\)</span> the emissions associated with power generation, and <span class="math-tex">\(e(f_i)\)</span> the associated fuel-specific emission factor for the installation. The installation-specific emission factor is calculated by <span class="math-tex">\(E_i^{el}/G_i\)</span>.</p> <p>Sources:</p> <p>[1] ENTSO-E Transparency Platform, <a href="https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show">Actual Generation per Generation Unit</a>.<br> [2] Bundesnetzagentur, <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Kraftwerksliste</a>.<br> [3] Umweltbundesamt, <a href="https://www.umweltbundesamt.de/dokument/datenbank-kraftwerke-in-deutschland">Datenbank Kraftwerke in Deutschland</a>.<br> [4] Umweltbundesamt, <a href="https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0">CO2-Emissionsfaktoren für fossile Brennstoffe</a>, Aktualisierung 2022.<br> [5] EU Union Registry, <a href="https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en#european-union-transaction-log">Verified Emissions 2021</a>.<br> [6] European Commission, <a href="https://climate.ec.europa.eu/system/files/2016-11/gd1_general_guidance_en.pdf"> Guidance Document n°1 on the harmonized free allocation methodology for the EU-ETS<br> post 2012</a>.<br> [7] Agora Energiewende, <a href="https://www.agora-energiewende.de/veroeffentlichungen/die-deutsche-braunkohlenwirtschaft-1/">Die deutsche Braunkohlenwirtschaft</a> (2017)<br> [8] Jan Frederick Unnewehr, Anke Weidlich, Leonhard Gfüllner, Mirko Schäfer, <a href="https://www.sciencedirect.com/science/article/pii/S2772783122000176">Open-data based carbon emission intensity signals for electricity generation in European countries – top down vs. bottom up approach</a>, Cleaner Energy Systems 3, 100018 (2022).</p> <p><br> </p> <p><br> </p>
Automated patent extraction powers generative modeling in focused chemical spaces: Training data and model checkpoints release
<p>Training data and model checkpoints accompanying paper on "Automated patent extraction powers generative modeling in focused chemical spaces". If you use this data, please cite the following manuscript:</p> <pre>@article{subramanian2023automated, title={Automated patent extraction powers generative modeling in focused chemical spaces}, author={Subramanian, Akshay and Greenman, Kevin P and Gervaix, Alexis and Yang, Tzuhsiung and G{\'o}mez-Bombarelli, Rafael}, journal={Digital Discovery}, year={2023}, publisher={Royal Society of Chemistry} }</pre>
Data for "Tracking physical delivery of electricity from generators to loads with power flow tracing"
<p>Data and analysis code for "Tracking physical delivery of electricity from generators to loads with power flow tracing".</p> <p>Data includes the following folders:</p> <ul> <li>validation: Ground truth generation data for 2020</li> <li>tracing: Power flow tracing results for one day</li> <li>secondary_analysis: Node-level power flow statistics</li> <li>networks: Solved networks, including Eastern U.S., Europe, Texas at 4 levels of clustering, and Europe with altered dispatch</li> <li>meta: between-node straight line distance for Eastern U.S. and Europe</li> <li>boundaries: shapefiles and per-node mappings for existing boundaries (Eastern U.S. and Europe) and example boundaries (Texas)</li> <li>analysis: physical delivery and deliverability metrics for all networks over all modeled days</li> </ul> <p>Code (`flow-delivery-analysis-main`) includes notebooks and helper functions for all visualizations and statistics in "Tracking physical delivery of electricity from generators to loads with power flow tracing". See the README for more information.</p>
A Study of ECHELON 3000 (Next Generation Powered Stapler) in General Abdominal and Thoracic Lung Resection Procedures
ClinicalTrials.gov study NCT05519215. IPD Sharing: YES. Countries: 1. Publications: 2.
Generation of Powerful Biological Tools for Understanding the Pathophysiology of Chronic Granulomatous Disease.
ClinicalTrials.gov study NCT02926963. IPD Sharing: NO. Countries: 1. Publications: 5.
Rainwater-driven microbial fuel cells for power generation in the remote areas' raw data
Open the record for dataset details and reuse information.
Data from: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
In 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural organisms. Despite nearly two decades of work since, evolved morphologies are not obviously more complex or natural, and the field seems to have hit a complexity ceiling. One hypothesis for the lack of increased complexity is that most work, including Sims', evolves morphologies composed of rigid elements, such as solid cubes and cylinders, limiting the design space. A second hypothesis is that the encodings of previous work have been overly regular, not allowing complex regularities with variation. Here we test both hypotheses by evolving soft robots with multiple materials and a powerful generative encoding called a compositional pattern-producing network (CPPN). Robots are selected for locomotion speed. We find that CPPNs evolve faster robots than a direct encoding and that the CPPN morphologies appear more natural. We also find that locomotion performance increases as more materials are added, that diversity of form and behavior can be increased with different cost functions without stifling performance, and that organisms can be evolved at different levels of resolution. These findings suggest the ability of generative soft-voxel systems to scale towards evolving a large diversity of complex, natural, multi-material creatures. Our results suggest that future work that combines the evolution of CPPN-encoded soft, multi-material robots with modern diversity-encouraging techniques could finally enable the creation of creatures far more complex and interesting than those produced by Sims nearly twenty years ago.
Experimental data for "Experimental Demonstration of Electric Power Generation from Earth's Rotation Through Its Own Magnetic Field"
Open the record for dataset details and reuse information.
Climate change impact on hydro power generation
<p>Impact of climate change on hydropower.</p>
Data from: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
Open the record for dataset details and reuse information.
Single-cell RNA sequencing unveils the hidden powers of zebrafish kidney for generating both hematopoiesis and adaptive antiviral immunity
GEO Series GSE242133. Danio rerio. 3 samples. Type: Expression profiling by high throughput sequencing.
Stylized Power Generator
A stylized, low poly steam engine power generator optimized for use in games. Source: Objaverse 1.0 / Sketchfab
Improving Family Well-Being and Child School Readiness: Power PATH Dual Generation Intervention
ClinicalTrials.gov study NCT02176590. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Supplementary Information datasets: Evaporation Reduction and Energy Generation Potential using Floating Photovoltaic Power Plants on the Aswan High Dam Reservoir
<p>These files contain supplementary information regarding the "Evaporation Reduction and Energy Generation Potential using Floating Photovoltaic Power Plants on the Aswan High Dam Reservoir"</p>
3D printing of reversible solid oxide cell stacks for efficient hydrogen production and power generation
Open the record for dataset details and reuse information.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.