An analytic massive total cross section of photon proton scattering is derived, which has geometric scaling. A geometric scaling is used to perform a global analysis of the deep inelastic scattering data on inclusive ...An analytic massive total cross section of photon proton scattering is derived, which has geometric scaling. A geometric scaling is used to perform a global analysis of the deep inelastic scattering data on inclusive structure function F2 measured in lepton-hadron scattering experiments at small values of Bjorken x. It is shown that the descriptions of the inclusive structure function F2 and longitudinal structure function FL are improved with the massive analytic structure function, which may imply the gluon saturation effect dominating the parton evolution process at HERA. The inclusion of the heavy quarks prevent the divergence of the lepton-hadron cross section, which plays a significant role in the description of the photoproduction region.展开更多
Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples.Metabolomics is emerging as a powerful tool generally for pre-cision medicine.Particularly,integrat...Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples.Metabolomics is emerging as a powerful tool generally for pre-cision medicine.Particularly,integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease.However,metabo-lomics data are very complicated.Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis.In this review article,we comprehensively review various methods that are used to preprocess and pretreat metabolo-mics data,including MS-based data and NMR-based data preprocessing,dealing with zero and/or missing values and detecting outliers,data normalization,data centering and scaling,data transformation.We discuss the advantages and limitations of each method.The choice for a suitable preprocessing method is determined by the biological hypothesis,the characteristics of the data set,and the selected statistical data analysis method.We then provide the perspective of their applications in the microbiome and metabolome research.展开更多
Local diversity AdaBoost support vector machine(LDAB-SVM) is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset...Local diversity AdaBoost support vector machine(LDAB-SVM) is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.展开更多
Bus reliability has long attracted attention and been extensively studied to enhance service quality.However,existing research generally evaluates bus reliability of specific routes or stops.To this end,this study exp...Bus reliability has long attracted attention and been extensively studied to enhance service quality.However,existing research generally evaluates bus reliability of specific routes or stops.To this end,this study explores en-route bus reliability with real-time data at network scale.Drawing on data of bus automatic vehicle location and smart card usage in Ningbo,China,this study calculates headway-based reliability with the difference between actual and scheduled headway at each stop.To demonstrate the trend of stop-level reliability along a bus route,reliability is graded and visualized on a map with ridership at each stop,which is then weighted with passenger-boarding volume.Route-level reliability is then quantified and mapped,where unreliable service basically concentrates in or extends through the centre area.With respect to network-level reliability,temporal changes are demonstrated with ridership on weekdays and at the weekend.It is observed that on weekdays,the reliability trend is similar to that of ridership,implying a causal relationship between bus travel-time variation and bus waiting-time at stops.Furthermore,a reliability comparison between weekdays in December and October shows the necessity of evaluating periodically and around important events to avoid negative riding experiences that discourage public transport usage.This research provides insights for bus agencies to systematically evaluate service reliability both spatially and temporarily,in order to identify and prioritize the routes and stops where the scope for reliability improvement and the expected benefit are greatest.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 11305040,11375071 and 11447203the Education Department of Guizhou Province Innovation Talent Fund under Grant No[2015]5508+2 种基金the Education Department of Guizhou Province Innovation Team Fund under Grant No[2014]35the Guizhou Province Science Technology Foundation under Grant No[2015]2114the Guizhou Province Innovation Talent Team Fund under Grant No[2015]4015
文摘An analytic massive total cross section of photon proton scattering is derived, which has geometric scaling. A geometric scaling is used to perform a global analysis of the deep inelastic scattering data on inclusive structure function F2 measured in lepton-hadron scattering experiments at small values of Bjorken x. It is shown that the descriptions of the inclusive structure function F2 and longitudinal structure function FL are improved with the massive analytic structure function, which may imply the gluon saturation effect dominating the parton evolution process at HERA. The inclusion of the heavy quarks prevent the divergence of the lepton-hadron cross section, which plays a significant role in the description of the photoproduction region.
基金supported by the Crohn's&Colitis Foundation Senior Research Award(No.902766 to J.S.)The National Institute of Diabetes and Digestive and Kidney Diseases(No.R01DK105118-01 and R01DK114126 to J.S.)+1 种基金United States Department of Defense Congressionally Directed Medical Research Programs(No.BC191198 to J.S.)VA Merit Award BX-19-00 to J.S.
文摘Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples.Metabolomics is emerging as a powerful tool generally for pre-cision medicine.Particularly,integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease.However,metabo-lomics data are very complicated.Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis.In this review article,we comprehensively review various methods that are used to preprocess and pretreat metabolo-mics data,including MS-based data and NMR-based data preprocessing,dealing with zero and/or missing values and detecting outliers,data normalization,data centering and scaling,data transformation.We discuss the advantages and limitations of each method.The choice for a suitable preprocessing method is determined by the biological hypothesis,the characteristics of the data set,and the selected statistical data analysis method.We then provide the perspective of their applications in the microbiome and metabolome research.
基金supported by the National Natural Science Foundation of China (60603098)
文摘Local diversity AdaBoost support vector machine(LDAB-SVM) is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.
文摘Bus reliability has long attracted attention and been extensively studied to enhance service quality.However,existing research generally evaluates bus reliability of specific routes or stops.To this end,this study explores en-route bus reliability with real-time data at network scale.Drawing on data of bus automatic vehicle location and smart card usage in Ningbo,China,this study calculates headway-based reliability with the difference between actual and scheduled headway at each stop.To demonstrate the trend of stop-level reliability along a bus route,reliability is graded and visualized on a map with ridership at each stop,which is then weighted with passenger-boarding volume.Route-level reliability is then quantified and mapped,where unreliable service basically concentrates in or extends through the centre area.With respect to network-level reliability,temporal changes are demonstrated with ridership on weekdays and at the weekend.It is observed that on weekdays,the reliability trend is similar to that of ridership,implying a causal relationship between bus travel-time variation and bus waiting-time at stops.Furthermore,a reliability comparison between weekdays in December and October shows the necessity of evaluating periodically and around important events to avoid negative riding experiences that discourage public transport usage.This research provides insights for bus agencies to systematically evaluate service reliability both spatially and temporarily,in order to identify and prioritize the routes and stops where the scope for reliability improvement and the expected benefit are greatest.