917 resultados para NNLO QCD, heavy quarks, loop-by-loop contributions, loop corrections, dimensional regularization


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An inverse food-web model for the western Antarctic Peninsula (WAP) pelagic food web was constrained with data from Palmer Long Term Ecological Research (PAL-LTER) project annual austral summer sampling cruises. Model solutions were generated for 2 regions with Adelie penguin Pygoscelis adeliae colonies presenting different population trends (a northern and a southern colony) for a 12 yr period (1995-2006). Counter to the standard paradigm, comparisons of carbon flow through bacteria, microzooplankton, and krill showed that the diatom-krill-top predator food chain is not the dominant pathway for organic carbon exchanges. The food web is more complex, including significant contributions by microzooplankton and the microbial loop. Using both inverse model results and network indices, it appears that in the northern WAP the food web is dominated by the microbial food web, with a temporal trend toward its increasing importance. The dominant pathway for the southern WAP food web varies from year to year, with no detectable temporal trend toward dominance of microzooplankton versus krill. In addition, sensitivity analyses indicated that the northern colony of Adelie penguins, whose population size has been declining over the past 35 yr, appears to have sufficient krill during summer to sustain its basic metabolic needs and rear chicks, suggesting the importance of other processes in regulating the Adelie population decline.

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Due to the impacts of natural processes and anthropogenic activities, different coastal wetlands are faced with variable patterns of heavy metal contamination. It is important to quantify the contributions of pollutant sources, in order to adopt appropriate protection measures for local ecosystems. The aim of this research was to compare the heavy metal contamination patterns of two contrasting coastal wetlands in eastern China. In addition, the contributions from various metal sources were identified and quantified, and influencing factors, such as the role of the plant Spartina alterniflora, were evaluated. Materials and methods Sediment samples were taken from two coastal wetlands (plain-type tidal flat at the Rudong (RD) wetland vs embayment-type tidal flat at Luoyuan Bay (LY)) to measure the content of Al, Fe, Co, Cr, Cu, Mn, Mo, Ni, Sr, Zn, Pb, Cd, and As. Inductively coupled plasma atomic emission spectrometry, flame atomic absorption spectrometry, and atomic fluorescence spectrometry methods were used for metal detection. Meanwhile, the enrichment factor and geoaccumulation index were applied to assess the pollution level. Principle component analysis and receptor modeling were used to quantify the sources of heavy metals. Results and discussion Marked differences in metal distribution patterns between the two systems were present. Metal contents in LY were higher than those in RD, except for Sr and Mo. The growth status of S. alterniflora influenced metal accumulations in RD, i.e., heavy metals were more easily adsorbed in the sediment in the following sequence: Cu > Cd > Zn > Cr > Al > Pb ≥ Ni ≥ Co > Fe > Sr ≥ Mn > As > Mo as a result of the presence and size of the vegetation. However, this phenomenon was not observed in LY. A higher potential ecological risk was associated with LY, compared with RD, except for Mo. Based on a receptor model output, sedimentary heavy metal contents at RD were jointly influenced by natural sedimentary processes and anthropogenic activities, whereas they were dominated by anthropogenic activities at LY. Conclusions A combination of geochemical analysis and modeling approaches was used to quantify the different types of natural and anthropogenic contributions to heavy metal contamination, which is useful for pollution assessments. The application of this approach reveals that natural and anthropogenic processes have different influences on the delivery and retention of metals at the two contrasting coastal wetlands. In addition, the presence and size of S. alterniflora can influence the level of metal contamination in sedimentary environments.