Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract usef...Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.展开更多
As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning...As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning,it can not achieve lane level positioning accuracy.Motivated by the mature ranging technologies such as radar and ultra-wideband(UWB),several cooperative positioning(CP)methods have been proposed to enhance the accuracy and robustness of GPS.In this paper,we proposed a twostage CP algorithm that combines multidimensional scaling(MDS)and Procrustes analysis for vehicles with GPS information.Specifically,the optimized MDS based on the scaling by majorizing a complicated function(SMACOF)algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions,then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information.All the computations are performed at the mobile edge computing node(MECN)for the request of ultra-reliable and low latency communications(URLLC).Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.展开更多
The catalyst layer （CL） of proton exchange mem-brane fuel cell （PEMFC） involves various particles and pores in meso-scale, which has an important effect on the mass, charge （proton and electron） and heat transpo...The catalyst layer （CL） of proton exchange mem-brane fuel cell （PEMFC） involves various particles and pores in meso-scale, which has an important effect on the mass, charge （proton and electron） and heat transport coupled with the electrochemical reactions. The coarse-grained molecular dynamics （CG-MD） method is employed as a meso-scale structure reconstruction technique to mimic the self-organization phenomena in the fabrication steps of a CL. The meso-scale structure obtained at the equilibrium state is further analyzed by molecular dynamic （MD） software to provide the necessary microscopic parameters for understanding of multi-scale and-physics processes in CLs. The primary pore size distribution （PSD） and active platinum （Pt） surface areas are also calculated and then compared with the experiments. In addition, we also highlight the implementation method to combine microscopic elementary kinetic reaction schemes with the CG-MD approaches to provide insight into the reactions in CLs. The concepts from CG modeling with particle hydrodynamics （SPH） and the problems on coupling of SPH with finite element modeling （FEM） methods are further outlined and discussed to understand the effects of the meso-scale transport phenomena in fuel cells.展开更多
文摘Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature.
基金This work was supported in part by the National Key Research and Development Program of China(2019YFB1600100)in part by the Foundation of Shaanxi Key Laboratory of Integrated and Intelligent Navigation under Grant SKLIIN-20190103.
文摘As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning,it can not achieve lane level positioning accuracy.Motivated by the mature ranging technologies such as radar and ultra-wideband(UWB),several cooperative positioning(CP)methods have been proposed to enhance the accuracy and robustness of GPS.In this paper,we proposed a twostage CP algorithm that combines multidimensional scaling(MDS)and Procrustes analysis for vehicles with GPS information.Specifically,the optimized MDS based on the scaling by majorizing a complicated function(SMACOF)algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions,then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information.All the computations are performed at the mobile edge computing node(MECN)for the request of ultra-reliable and low latency communications(URLLC).Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.
文摘The catalyst layer （CL） of proton exchange mem-brane fuel cell （PEMFC） involves various particles and pores in meso-scale, which has an important effect on the mass, charge （proton and electron） and heat transport coupled with the electrochemical reactions. The coarse-grained molecular dynamics （CG-MD） method is employed as a meso-scale structure reconstruction technique to mimic the self-organization phenomena in the fabrication steps of a CL. The meso-scale structure obtained at the equilibrium state is further analyzed by molecular dynamic （MD） software to provide the necessary microscopic parameters for understanding of multi-scale and-physics processes in CLs. The primary pore size distribution （PSD） and active platinum （Pt） surface areas are also calculated and then compared with the experiments. In addition, we also highlight the implementation method to combine microscopic elementary kinetic reaction schemes with the CG-MD approaches to provide insight into the reactions in CLs. The concepts from CG modeling with particle hydrodynamics （SPH） and the problems on coupling of SPH with finite element modeling （FEM） methods are further outlined and discussed to understand the effects of the meso-scale transport phenomena in fuel cells.
