Top 10 poverty.com.pk Wettbewerber

worldpoverty.io

Explore poverty data by age, gender, and region for every country in the world until 2030

zero-poverty.in

zero poverty program

srso.org.pk

Sindh Rural Support Organization (SRSO) is a leading non-profit in Sindh, Pakistan, empowering rural communities through financial services, education...

comparativepopulationstudies.de

comparative population studies open access online journal

bmcpublichealth.biomedcentral.com

BMC Public Health is an open access, peer-reviewed journal publishing research in all aspects of public health, including the social determinants of health, ...

dlib.org

d-lib magazine is an electronic publication with a focus on digital library research and development, including new technologies, applications, and contextual social and economic issues.

sci-lib.com

последние новости науки и техники от ведущих ученых с мировым именем в большой научной библиотеке

os.copernicus.org

Abstract. In the last 2 decades, UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from operational applications in physics and biogeochemistry to fundamental theory. We highlight the emergence of strong collaboration in the UK MDA community over this period and the increasing unification of its tools. We focus on identifying the MDA stakeholder community and current/future areas of impact, as well as current trends and future opportunities. This includes the rapid growth of machine learning (ML)/artificial intelligence (AI) and digital-twin applications. We articulate a vision for the future, including the need for future types of observational data (whether planned missions or hypothetical) and how the community should respond to increases in computational power and new computer architectures (e.g. exascale computing). We contrast the requirements of different MDA areas, including physics, biogeochemistry, and coupled data assimilation (DA). Although the specifics of the vision depend on each area, common themes emerge. We advocate for balanced redistribution of new computational capability among increased model resolution, model complexity, more sophisticated DA algorithms, and uncertainty representation (e.g. ensembles). We also advocate for integrated approaches, such as strongly coupled DA (ocean–atmosphere, physics–biogeochemistry, and ocean–sea ice) and the use of ML/AI components (e.g. for multivariate increment balancing, bias correction, model emulation, observation re-gridding, or fusion).

freemedicaljournals.com

over 4000 free medical journals. daily presentations of new journals. free subscription to the journal alerts.