References

  1. F. Ye, G.H. Fang, J.L. Jin, Evaluation model of water resources carrying capacity based on grey cluster set pair analysis method, J. Water Resour. Water Eng., 31 (2020) 30–36.
  2. D. Yang, Y. Yang, J. Xia, Hydrological cycle and water resources in a changing world: a review, Geogr. Sustainability, 2 (2021) 115–122.
  3. O. Bozorg-Haddad, B. Zolghadr-Asli, P. Sarzaeim, M. Aboutalebi, X. Chu, H.A. Loáiciga, Evaluation of water shortage crisis in the Middle East and possible remedies, J. Water Supply Res. Technol. AQUA, 69 (2020) 85–98.
  4. L. Yin, H. Zhang, Z. Tang, J. Xu, D. Yin, Z. Zhang, X. Yuan, M. Zhu, S. Zhao, X. Li, X. Liu, rMVP: a memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study, Genomics Proteomics Bioinf., 19 (2021) 619–628.
  5. A.P. Boughton, R.P. Welch, M. Flickinger, P. VandeHaar, D. Taliun, G.R. Abecasis, M. Boehnke, LocusZoom.js: interactive and embeddable visualization of genetic association study results, Bioinformatics, 37 (2021) 3017–3018.
  6. H. Zhang, Z. Wang, J. Liu, J. Chai, C. Wei, Selection of targeted poverty alleviation policies from the perspective of land resources-environmental carrying capacity, J. Rural Stud., 93 (2022) 318–325.
  7. M. Świąder, D. Lin, S. Szewrański, J.K. Kazak, K. Iha, J. van Hoof, I. Belčáková, S. Altiok, The application of ecological footprint and biocapacity for environmental carrying capacity assessment: a new approach for European cities, Environ. Sci. Policy, 105 (2020) 56–74.
  8. S. Zhao, E.R. Zettler, L.A. Amaral-Zettler, T.J. Mincer, Microbial carrying capacity and carbon biomass of plastic marine debris, The ISME J., 15 (2021) 67–77.
  9. I.A. Guiamel, H.S. Lee, Watershed modelling of the Mindanao River Basin in the Philippines using the SWAT for water resource management, Civ. Eng. J., 6 (2020) 626–648.
  10. M. Janga Reddy, D. Nagesh Kumar, Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review, H2Open J., 3 (2020) 135–188.
  11. H. Eer, L. Ma, X. Xie, J. Ma, X. Ma, C. Yue, Q. Ma, X. Liang, W. Ding, Y. Li, Genetic polymorphism association analysis of SNPs on the species conservation genes of Tan sheep and Hu sheep, Trop. Anim. Health Prod., 52 (2020) 915–926.
  12. L. Jiang, Z. Zheng, H. Fang, J. Yang, A generalized linear mixed model association tool for biobank-scale data, Nat. Genet., 53 (2021) 1616–1621.
  13. F. Bhuiyan, K. Baird, R. Munir, The association between organisational culture, CSR practices and organisational performance in an emerging economy, Meditari Accountancy Res., 28 (2020) 977–1011.
  14. H.W. Kamran, A.A. Pantamee, A.K. Patwary, T.A. Ghauri, P.D. Long, D.Q. Nga, Measuring the association of environmental, corporate, financial, and social CSR: evidence from fuzzy TOPSIS nexus in emerging economies, Environ. Sci. Pollut. Res., 28 (2021) 10749–10762.
  15. X. Peng, X. Li, X. Yang, Analysis of circular economy of E-commerce market based on grey model under the background of big data, J. Enterp. Inf. Manage., 35 (2022) 1148–1167.
  16. B.J. Moggridge, R.M. Thompson, Cultural value of water and western water management: an Australian indigenous perspective, Australas. J. Water Resour., 25 (2021) 4–14.
  17. T. Aawar, D. Khare, Assessment of climate change impacts on streamflow through hydrological model using SWAT model: a case study of Afghanistan, Model. Earth Syst. Environ., 6 (2020) 1427–1437.
  18. J.B.S.O. de Andrade Guerra, I.I. Berchin, J. Garcia, S. da Silva Neiva, A.V. Jonck, R.A. Faraco, W.S. de Amorim, J.M.P. Ribeiro, A literature-based study on the water–energy–food nexus for sustainable development, Stochastic Environ. Res. Risk Assess., 35 (2021) 95–116.
  19. M.-C. Li, Z. Tang, C. Liu, R. Huang, M.S. Koo, G. Zhou, Q. Wu, Water-redispersible cellulose nanofiber and polyanionic cellulose hybrids for high-performance water-based drilling fluids, Ind. Eng. Chem. Res., 59 (2020) 14352–14363.
  20. C. Giudicianni, M. Herrera, A. di Nardo, K. Adeyeye, Automatic multiscale approach for water networks partitioning into dynamic district metered areas, Water Resour. Manage., 34 (2020) 835–848.
  21. Z. Wang, P. Wei., IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration, Bioinformatics, 36 (2020) 5439–5447.
  22. K.R. Chng, T.S. Ghosh, Y.H. Tan, T. Nandi, I.R. Lee, A.H.Q. Ng, C. Li, A. Ravikrishnan, K.M. Lim, D. Lye, T. Barkham, K. Raman, S.L. Chen, L. Chai, B. Young, Y.-H. Gan, N. Nagarajan, Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut, Nat. Ecol. Evol., 4 (2020) 1256–1267.
  23. C. Kulworatit, S. Tuntiwongwanich, The use of digital intelligence and association analysis with data mining methods to determine the factors affecting digital safety among Thai adolescents, Int. J. Innovation Creativity Change, 14 (2020) 1120–1134.
  24. J. Wu, R. Yu, H. Wang, C. Zhou, S. Huang, H. Jiao, S. Yu, X. Nie, Q. Wang, S. Liu, S. Weining, R.P. Singh, S. Bhavani, Z. Kang, D. Han, Q. Zeng, A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments, Plant Biotechnol. J., 19 (2021) 177–191.
  25. Y. Xia, Correlation and association analyses in microbiome study integrating multiomics in health and disease, Prog. Mol. Biol. Transl. Sci., 171 (2020) 309–491.