A phosphite ligand modified heterogeneous catalyst was developed for the hydroformylation of internal olefins to linear aldehydes, which showed a high activity and high regioselectivity and could be separated easily b...A phosphite ligand modified heterogeneous catalyst was developed for the hydroformylation of internal olefins to linear aldehydes, which showed a high activity and high regioselectivity and could be separated easily by filtration after reaction in an autoclave. Three nanoporous silica sieves were used to investigate the influence of pore structure and shape selective performance of support on the regioselectivity to the linear products.展开更多
In recent years,the assessment of proprioceptive function has received increased attention in clinical and motor skill research.This is not surprising given the growing body of scientific evidence on the importance of...In recent years,the assessment of proprioceptive function has received increased attention in clinical and motor skill research.This is not surprising given the growing body of scientific evidence on the importance of proprioceptive information for controlling nearly all facets of human movement;from standing to performing highly skilled movement patterns展开更多
Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential rel...Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.展开更多
基金Supported by the Ministry of Science and Technology of China(No.2009CB623503)
文摘A phosphite ligand modified heterogeneous catalyst was developed for the hydroformylation of internal olefins to linear aldehydes, which showed a high activity and high regioselectivity and could be separated easily by filtration after reaction in an autoclave. Three nanoporous silica sieves were used to investigate the influence of pore structure and shape selective performance of support on the regioselectivity to the linear products.
基金supported by the Deutsche Forschungsgemeinschaft (No.KR 4595/1-1)
文摘In recent years,the assessment of proprioceptive function has received increased attention in clinical and motor skill research.This is not surprising given the growing body of scientific evidence on the importance of proprioceptive information for controlling nearly all facets of human movement;from standing to performing highly skilled movement patterns
基金mainly supported by the National Natural Science Foundation of China(Nos.61125201,61303070,and U1435219)
文摘Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.
