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3,472 results for “Balancing”
Monthly accumulated sublimation and yearly accumulated surface mass balance (SMB) components RACMO model simulations for Antarctica on 27km grid for 2000-2012
<p>Monthly accumulated (denoted monthlyS) sublimation components and yearly accumulated (denoted yearlyS) surface mass balance (SMB) components for Antarctica (ANT) on 27 km horizontal grid produced by RACMO model are presented in this dataset for the year 2000-2012. The dataset consists of data from three simulations named, NODRIFT, Rp3, and RpNew. NODRIFT represents the run with no blowing snow sublimation, Rp3 corresponds to version of the blowing snow model with simplifications, RpNew corresponds to the advanced version with new updates to the blowing snow model in RACMO. Details of the simulations can be found in the associated paper : <a title="Contribution of blowing snow sublimation to the surface mass balance of Antarctica" href="https://doi.org/10.5194/egusphere-2024-116" target="_blank" rel="noopener">https://doi.org/10.5194/egusphere-2024-116</a>. The data includes yealy accumulated SMB components including SMB, snow melt, refreezing, precipiation, runoff, blowing snow erosion, surface sublimation, and blowing snow sublimation, the data also includes yearly averaged (denoted yearlyA) . Furthermore, the data includes monthly accumulated sublimation components of surface sublimation (subl), and blowing snow sublimation (suds). </p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Figure 2 in Equity and career-life balance in marine mammal science?
Figure 2. Survey results for the cumulative amount of time spent away from home due to work-related activities (e.g., fieldwork or conferences) in any year for men (green) and women (yellow).
Figure 1 in Equity and career-life balance in marine mammal science?
Figure 1. Percentage of men (green) and women (yellow) survey respondents employed in common types of marine mammal science careers.
Figure 9. Experimental Page Rank dependency on Markov Chain length with balanced distribution-Study of a Random Navigation on the Web Using Software Simulation
<p>This paper explored different implementation choices for analyzing the most important<br> parameters about a web. Many researchers explored the use of new search engines for studying the<br> evolution of the web (Ntoulas, Cho and Olston, 2004). Another important research is realized about<br> the Link Structure Graph (LSG). The LSG captures a complete hyperlink structure from the web<br> and models link associations reflected in the page layout (Rodrigues, Milic-Frayling and Fortuna,<br> 2007). For further works ideas like extrapolation methods for accelerating page rank calculation can<br> be developed (Kamvar et al., 2003).</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE paper)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the journal "Concurrency and Computation: Practice and Experience", under the title <em>"Performance Modeling of a Geophysics Application to Accelerate the Tuning of Over-decomposition Parameters through Simulation"</em>.</p>
Antarctic surface mass balance with the regional climate model MAR (1979–2015)
<p>Outputs of the regional climate model MAR v3.6.41 for Antarctica, resolution 35km + source code</p> <p>===========================================</p> <p>Cécile Agosta, 23 Jan 2019 </p> <p>cecile.agosta@gmail.com</p> <p>===========================================</p> <p>Grid specifications are given in MAR-ant35km-grid.nc (projection : EPSG 3031).</p> <p>* State variables are averages of daily means:</p> <p> TT > temperature (°C)</p> <p> ZZ > height above sea level (m)</p> <p> UU, VV > x-wind and y-wind in the stereographic grid (m s-1)</p> <p> UV > wind speed (m s-1)</p> <p>State variables ending with z (e.g. UUz) are interpolated on fixed altitude levels above the ground.</p> <p>State variables ending with p (e.g. UUp) are interpolated on fixed pressure levels.</p> <p>* SMB components are summed: kg m-2 month-1 for montly files, kg m-2 year-1 for annual files, kg m-2 year-1 for clim files</p> <p> snf > snowfall</p> <p> rnf > rainfall</p> <p> rof > run-off</p> <p> sbl > sublimation/condensation</p> <p> smb = snf + rnf - sbl - rof</p> <p> mlt > snowmelt</p> <p> rfz > refreezing</p> <p>If you use this data, please cite the final accepted version of this article:</p> <p>Agosta C., Amory C., Kittel C., Orsi A., Favier V., Gallée H., van den Broeke M.R., Lenaerts J.T., van Wessem J.M., & Fettweis X. (in review, 2018). Estimation of the Antarctic surface mass balance using MAR (1979-2015) and identification of dominant processes. <em>The Cryosphere Discussions</em>, 1–22, <a href="https://doi.org/10.5194/tc-2018-76">doi:10.5194/tc-2018-76</a>.</p> <p>Please contact me if you need other outputs (variables/daily or hourly time steps)</p>
Greenland monthly surface mass balance 1840-2012
<p>This surface mass balance (SMB) product is based on Box (2013), with refinements described by Schlegel et al. (2016), Appendix. It includes monthly estimates of accumulation and melt, as well as estimates for SMB error for the historical period (1960-2012).</p> <p>It is the results of a calibration of observational data to regional climate model (RCM) output, in this case RACMO2.3 (Noël et al., 2016). The calibration for temperature (T) and SMB components is based on a 53-year overlap period (1960-2012). Note that the overlap period for the calibration of snow accumulation rate is shorter, since ice core data availability drops after 1999. Calibration is made using linear regression coefficients for 5 km grid cells that match the average of the reconstruction to RACMO2.3. The RACMO2.3 output are resampled and reprojected from the native 0.1 deg (~10 km) grid to a 5 km grid better resolving areas where sharp gradients occur, especially near the ice margin where mass fluxes are largest. </p>
Monolayer doping of silicon-germanium alloys: A balancing act between phosphorus incorporation and strain relaxation
<p>This paper presents the application of monolayer doping (MLD) to silicon-germanium (SiGe). This study was carried out for phosphorus dopants on wafers of epitaxially grown thin films of strained SiGe on silicon with varying concentrations of Ge (18%, 30%, and 60%). The challenge presented here is achieving dopant incorporation while minimizing strain relaxation. The impact of high temperature annealing on the formation of defects due to strain relaxation of these layers was qualitatively monitored by cross-sectional transmission electron microscopy and atomic force microscopy prior to choosing an anneal temperature for the MLD drive-in. Though the bulk SiGe wafers provided are stated to have 18%, 30%, and 60% Ge in the epitaxial SiGe layers, it does not necessarily mean that the surface stoichiometry is the same, and this may impact the reaction conditions. X-ray photoelectron spectroscopy (XPS) and angle-resolved XPS were carried out to compare the bulk and surface stoichiometry of SiGe to allow tailoring of the reaction conditions for chemical functionalization. Finally, dopant profiling was carried out by secondary ion mass spectrometry to determine the impurity concentrations achieved by MLD. It is evident from the results that phosphorus incorporation decreases for increasing mole fraction of Ge, when the rapid thermal annealing temperature is a fixed amount below the melting temperature of each alloy.</p>
BALANCE database
<p>This data set includes: </p> <p>- Stakeholders active with the SOC technology (either SOEC, SOFC or rSOC).</p> <p>- National and EU regional funding programme supporting SOC technology.</p> <p>- European project supporting rSOC/SOEC technology</p> <p>- List of demonstration plants for energy storage through power-to-X.</p> <p>The data have been gather within the H2020 project BALANCE and they might not be exhaustive.</p> <p><a href="https://www.balance-project.org/">https://www.balance-project.org/</a></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731224.</p>
Figure 6 in Comparison of microplankton heterotrophic-photoautotrophic balance based on the content of ATP and chlorophyll a in the plankton of the northern area of the Black Sea during the autumn and spring seasons
Figure 6. Distribution of microplankton HPI in the photic zone of the Crimean coastal waters and deep-water northern part of the Black Sea at April 2017.
Figure 4 in Comparison of microplankton heterotrophic-photoautotrophic balance based on the content of ATP and chlorophyll a in the plankton of the northern area of the Black Sea during the autumn and spring seasons
Figure 4. Distribution of microplankton HPI in the photic zone of the Crimean coastal waters and deep-water northern part of the Black Sea at October 2016.
Figure 3 in Comparison of microplankton heterotrophic-photoautotrophic balance based on the content of ATP and chlorophyll a in the plankton of the northern area of the Black Sea during the autumn and spring seasons
Figure 3. Distribution of microplankton HPI in the surface waters of the Crimean coastal waters and deep-water northern part of the Black Sea at October 2016.
Figure 1 in Comparison of microplankton heterotrophic-photoautotrophic balance based on the content of ATP and chlorophyll a in the plankton of the northern area of the Black Sea during the autumn and spring seasons
Figure 1. Distribution of microplankton chlorophyll a and ATP concentrations in the Crimean coastal waters and deepwaternorthern part of the Black Sea at October 2016.
FIGURE 1 in Four mammal fossil calibrations: balancing competing palaeontological and molecular considerations
FIGURE 1. The phylogenetic placement of reference fossils (full red arrows) relative to modern groups for 1, Macropodoidea; 2, Monotremata; 3 Caviomorpha-Phiomorpha; and 4, Chiroptera. These clades are shaded grey and black circles identify the calibrated nodes. Relationships among modern taxa are taken from the DNA analysis of Meredith et al. (2011), although each is supported by a broad consensus of molecular phylogenetic studies. Dotted red arrows (1, 3, 4) indicate possible alternative placements for the reference fossil that if they can be confirmed, would offer (tighter) calibration of a slightly younger node.
Dataset for the article Maintaining favourable carbon balance in boreal clay soil is challenging even under no-till and crop diversification
Open the record for dataset details and reuse information.
Great Lakes monthly water balance components from the Large Lakes Statistical Water Balance Model (L2SWBM)
<p>**Note that an updated version of the data (v3.0) was uploaded on October 2, 2024, which supersedes earlier versions**</p> <p>These data sets are the results of leveraging bi-national data and the Large Lakes Statistical Water Balance Model (L2SWBM) specifically tailored for the Laurentian Great Lakes to produce value-added time series of water supply components, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. </p> <p>The model serves as a new cornerstone for bi-national coordination of hydrologic data throughout this international transboundary basin, providing an improved means of capturing data patterns, revealing seasonal variabilities, as well as short-term and long-term trends.</p> <p>This repository includes monthly output from the L2SWBM. Output datasets include over-lake precipitation, over-lake evaporation, lateral tributary inflow (runoff), connecting channel flow (cms and also included in mm normalized to lake area), diversion flow (cms and also included in mm normalized to lake area), and component net basin supply. Data is included for lakes Superior, Michigan-Huron, Erie, and Ontario.</p> <p>This version contains data from 1950 to 2022.</p>
Projected surface mass balance of the Juneau Icefield, Southeast Alaska (2030 to 2060, RCP8.5)
<p>This dataset contains the modelled projected surface mass balance of the Juneau Icefield in Southeast Alaska for two global climate models (GFDL-CM3 and NCAR-CCSM4) for the RCP8.5 emissions scenario from 2030 to 2060. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
Past surface mass balance of the Juneau Icefield, Southeast Alaska (1980 to 2019)
<p>This dataset contains the modelled past surface mass balance of the Juneau Icefield in Southeast Alaska for three models. A reanalysis model, CFSR from 1980 to 2019. And two global climate models (GFDL-CM3 and NCAR-CCSM4) from 1980 to 2010. The simulated surface mass balance was produced using COSIPY - a coupled snowpack and ice surface energy and mass balance model. The input climate data was originally dynamically downscaled in Lader et al., (2020, <em>J. Appl. Meteor. Climatol.)</em></p> <p>This data supplements the manuscript "<em>Surface mass balance modelling of the Juneau Icefield highlights the potential for rapid ice loss by the mid-21st century</em>" submitted to the Journal of Glaciology. </p>
WORK-LIFE BALANCE OF THE EMPLOYED POPULATION DURING THE EMERGENCY SITUATION OF COVID-19 IN LATVIA
<p>All the employees face the challenge of finding the right work-life balance. The ability of employees to deal with the successful combining of work, family responsibilities, and personal life is crucial for both employers and family members of employees. During the COVID-19 emergency situation, many people around the world were forced to work remotely. Initially, there were observed some certain expectations about the possibility of working from home as a positive factor that will promote work-life balance. However, over time, negative tendencies were also revealed, as employees were only one call or message away from the employer, and uncertainty and leisure time with family often created more stress. As many organizations and individuals were not ready for this sudden change, many mistakes were made, which further raised the issue of work-life balance. The aim of the research was to evaluate the flexibility of reconciling work and private life of Latvian employees in various socio-demographic groups during the COVID-19 emergency situation in spring 2020, to investigate how family life influenced employees’ ability to perform work duties, to find out if employees had any additional housework responsibilities and how their workload changed concerning housework amount during the COVID-19 emergency situation. The research is based on the data obtained in the survey of the Latvian employed population, which was conducted within the framework of the Latvian National Research Programme Project “CoLife” in the second half of 2020. As a result, the hypothesis of the research that all groups of employees experienced work-life balance difficulties during the COVID-19 emergency situation has been partially confirmed, i.e. women in the 18-44 age group and respondents with minor children in the household more likely faced difficulties of work-life balance. The scientific research methods that were used in the research are the monographic method, content analysis, survey, data processing with SPSS to determine the mutual independence of the data from the questionnaires. </p>
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Allen Brain Atlas
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OpenNeuro
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