Showing 1,581 - 1,600 results of 21,342 for search '(( significantly ((better decrease) OR (mean decrease)) ) OR ( significant decrease decrease ))', query time: 0.58s Refine Results
  1. 1581

    Summary statistics of key variables. by Saul Estrin (8629173)

    Published 2024
    “…We propose that, while there are still agglomeration benefits, the development path followed by cities in developing countries also creates significant agglomeration costs and these act to limit innovation. …”
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    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)

    Published 2024
    “…<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.…”
  5. 1585

    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)

    Published 2024
    “…<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.…”
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    Data_GDP/ Ndvi. by Qianhong Mao (22305184)

    Published 2025
    “…The findings include the following: (1) from 2010 to 2022, the spatial structure of urban-fringe-rural areas in Suzhou changed considerably, with 69.04% rural areas transformed into fringe areas, and 50.83% fringe areas developed into urban areas; (2) based on transition process, the region was further divided into urban maintenance, urban expansion, fringe maintenance, fringe expansion, and rural retention areas. Most of the mean value of ESs showed a gradient increasing differences along urban-fringe-rural, while the greatest decrease occurs in fringe expansion and urban expansion areas; and (3) interactions for changes in ES pairs also more closely linked in these two regions, with synergies dominating. …”