Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimizatio...Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimization. We analyze how the prediction error varies with different parameters （σ, γ） in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.展开更多
Based on the formalism of Keldysh's nonequilibrium Green function, we establish a two momenta spinor Boltzmann equation for longitudinal scalar distribution function and transverse vector distribution function. Th...Based on the formalism of Keldysh's nonequilibrium Green function, we establish a two momenta spinor Boltzmann equation for longitudinal scalar distribution function and transverse vector distribution function. The longitudinal charge currents, transverse spin currents and the continuity equations satisfied by them are then studied, it indicates that both the charge currents and spin currents decay oscillately along with position, which is due to the momenta integral over the Fermi surface. We also compare our charge currents and spin currents with the corresponding results of one momentum spinor Boltzmann equation, the differences are obvious.展开更多
Aims Individual growth constitutes a major component of individual fitness.However,measuring growth rates of herbaceous plants non-destructively at the individual level is notoriously difficult.This study,based on an ...Aims Individual growth constitutes a major component of individual fitness.However,measuring growth rates of herbaceous plants non-destructively at the individual level is notoriously difficult.This study,based on an accurate non-destructive method of aboveground biomass estimation,aims to assess individual relative growth rates(RGRs)of some species,identify its environmental drivers and test its consequences on community patterning.We specifically address three questions:(i)to what extent environmental conditions explain differences in individual plant growth between sites,(ii)what is the magnitude of intraspecific variability of plant individual growth within and between sites and(iii)do species-averaged(dis-)advantage of individual growth compared with the whole vegetation within a site correlate with species ranking at the community level?Methods We monitored the growth of individuals of four common perennial species in 18 permanent grasslands chosen along a large pedoclimatic gradient located in the Massif Central,France.We measured soil properties,levels of resources and meteorological parameters to characterize environmental conditions at the site level.This design enables us to assess the influence of environmental conditions on individual growth and the relative extent of inter-individual variability of growth explained within and between sites.We determined the ranking of each of the four species in each site with botanical surveys to assess the relationship between species-averaged growth(dis-)advantage relative to the whole community and species rank in the community.Important Findings We found that environmental conditions explain a significant proportion of individual growth variability,and that this proportion is strongly variable between species.Light availability was the main driver of plant growth,followed by rainfall amount and potential evapotranspiration,while soil properties had only a slight effect.We further highlighted a moderate to high within-site inter-individual variability of growth.We finally showed that there was no correlation between species ranking and species-averaged individual growth.展开更多
基金The project supported by National Natural Science Foundation of China under Grant No. 90203008 and the Doctoral Foundation of the Ministry of Education of China
文摘Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimization. We analyze how the prediction error varies with different parameters （σ, γ） in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 11274378 and 10404037
文摘Based on the formalism of Keldysh's nonequilibrium Green function, we establish a two momenta spinor Boltzmann equation for longitudinal scalar distribution function and transverse vector distribution function. The longitudinal charge currents, transverse spin currents and the continuity equations satisfied by them are then studied, it indicates that both the charge currents and spin currents decay oscillately along with position, which is due to the momenta integral over the Fermi surface. We also compare our charge currents and spin currents with the corresponding results of one momentum spinor Boltzmann equation, the differences are obvious.
基金supported by the Region Auvergne-Rhône-Alpes and the European Regional Development Fund(FEDER)(grant no.AV0008781).
文摘Aims Individual growth constitutes a major component of individual fitness.However,measuring growth rates of herbaceous plants non-destructively at the individual level is notoriously difficult.This study,based on an accurate non-destructive method of aboveground biomass estimation,aims to assess individual relative growth rates(RGRs)of some species,identify its environmental drivers and test its consequences on community patterning.We specifically address three questions:(i)to what extent environmental conditions explain differences in individual plant growth between sites,(ii)what is the magnitude of intraspecific variability of plant individual growth within and between sites and(iii)do species-averaged(dis-)advantage of individual growth compared with the whole vegetation within a site correlate with species ranking at the community level?Methods We monitored the growth of individuals of four common perennial species in 18 permanent grasslands chosen along a large pedoclimatic gradient located in the Massif Central,France.We measured soil properties,levels of resources and meteorological parameters to characterize environmental conditions at the site level.This design enables us to assess the influence of environmental conditions on individual growth and the relative extent of inter-individual variability of growth explained within and between sites.We determined the ranking of each of the four species in each site with botanical surveys to assess the relationship between species-averaged growth(dis-)advantage relative to the whole community and species rank in the community.Important Findings We found that environmental conditions explain a significant proportion of individual growth variability,and that this proportion is strongly variable between species.Light availability was the main driver of plant growth,followed by rainfall amount and potential evapotranspiration,while soil properties had only a slight effect.We further highlighted a moderate to high within-site inter-individual variability of growth.We finally showed that there was no correlation between species ranking and species-averaged individual growth.
