The following publications have been captured over the last month and University of Leicester publications linked to either the Newton Fund or Global Challenges Research Fund. Both funds are supported as Official Development Assistance (ODA) by the UK Government.
(Data are updated on the first day of each month)
The following information was updated on 1 September 2021 by the DevPubMetric system using data captured by the Scopus database.
There are 7 publications included in this list.
Berenguer E., Lennox G., Ferreira J., Malhi Y., Aragão L., Barreto J.R., Del Bon Espírito-Santo F., Figueiredo A.E.S., França F., Gardner T., et al. (2021), Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests, Proceedings of the National Academy of Sciences of the United States of America 118: https://doi.org/10.1073/pnas.2019377118
Cromwell E.A., Osborne J.C.P., Unnasch T.R., Basáñez M.G., Gass K., Barbre K.A., Hill E., Johnson K., Donkers K.M., Shirude S., et al. (2021), Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning, PLoS Neglected Tropical Diseases 15: https://doi.org/10.1371/journal.pntd.0008824
Stimpson C.M., O'Donnell S., Huong N.T.M., Holmes R., Utting B., Kahlert T., Rabett R.J. (2021), Confirmed archaeological evidence of water deer in Vietnam: Relics of the Pleistocene or a shifting baseline?, Royal Society Open Science 8: https://doi.org/10.1098/rsos.210529
Wang S.H., Celebi M.E., Zhang Y., Yu X., Lu S., Yao X., Zhou Q., Miguel M.G., Tian Y., Gorriz J.M., et al. (2021), Advances in data preprocessing for bio-medical data fusion: An overview of the methods, challenges, and prospects, Information Fusion 76: 376-421 https://doi.org/10.1016/j.inffus.2021.07.001
Yu X., Zhou Q., Wang S.H., Zhang Y. (2021), A systematic survey of deep learning in breast cancer, International Journal of Intelligent Systems : https://doi.org/10.1002/int.22622
Zhang Y., Dong Z., Gorriz J.M., Cattani C., Yang M. (2021), Introduction to the special issue on recent advances on deep learning for medical signal analysis, CMES - Computer Modeling in Engineering and Sciences 128: 399-401 https://doi.org/10.32604/cmes.2021.017472
Zimbres B., Rodríguez-Veiga P., Shimbo J., da Conceição Bispo P., Balzter H., Bustamante M.M.C., Roitman I., Haidar R.F., Miranda S., Gomes L., et al. (2021), Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome, Forest Ecology and Management 499: https://doi.org/10.1016/j.foreco.2021.119615
Data were captured and processed by the DevPubMetric system. The list of publications was generated from data reported by projects directly to UKRI through ResearchFish
Publication data were captured from the UKRI Gateway to Research system.
Open Government Licence
Additional records were generated using the Scopus database through structured searches for new documents by programme name and project reference.All records were then processed to collate full bibliographic data downloaded from Scopus.
This list of publications only contains documents that can be matched against source publications that are included in the Scopus database and the identification process requires researchers to have either reported the outcome to ResearchFish or to have included the programme name or project reference number in a publication's acknowledgement.
It is recognised that a small number of publications reported by researchers in this way and then captured by the DevPubMetric process may have limited levels of direct attribution to the programme.
This list of publications is generated through an automated process. It is possible that a few publications may be included which should not be attributed to this list and that some others may have been missed. Please send requests for corrections to admin@pvgglobal.uk