Proportion of the increase (green) or decrease (red) in forest cover in areas with different fire recurrences in Africa, according to the Mann-Kendall test and statistically significance values. by Oswaldo Maillard (20660661)

The contemporary, long-term seasonality in inundation over the whole of the Amazon basin. by Nic Gedney (19762301)

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Wetlands over the lowland Amazon basin from the Hess product [19,20]. by Nic Gedney (19762301)

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Decadal seasonality of the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

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Percentage regional changes in decadal regional hydrology. by Nic Gedney (19762301)

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Projected decadal changes in seasonality in the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

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Changes in decadal precipitation and JULES inundation between 1990–1999 and 2089–2098. by Nic Gedney (19762301)

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Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Average % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for brown non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for brown non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Summary of results based on the results of Mancova’s analysis, highlighting the relationship between the acoustic parameters of the three studied species and the noise gradient rec... by Rogério Ferreira de Oliveira (11133750)

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Simulated regional mean wetland seasons. by Nic Gedney (19762301)

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Regional contemporary (1990–1999 mean) precipitation and JULES inundation fraction over the Amazon basin. by Nic Gedney (19762301)

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Decadal mean regional hydrology. by Nic Gedney (19762301)

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Wetland classification using the ensemble simulations. by Nic Gedney (19762301)

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Wetland classification scheme for the contemporary period. by Nic Gedney (19762301)

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Contemporary mean inundation fraction in the Amazon basin. by Nic Gedney (19762301)

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ICER changes with time horizon. by Yiping An (20609789)

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Changes of exchangeable calcium carbonate content during acification of black soil. by Xiaoxu Zhu (11764430)

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Green Hydrogen Economy: Scenarios versus Technologies by Hua Fan (495346)

“…Scenarios employing off-grid renewable energy without battery storage, coupled with low-capital-expenditure electrolyzers and optimized maintenance, offer the greatest cost benefits. This framework provides a basis for green hydrogen system design, highlighting the critical role of operational optimization in accelerating cost-effective deployment.…”