Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and qual...Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and quality.Design/methodology/approach:The institutional ranking process developed here considers all institutions in all countries and regions,thereby including those that are established,as well as those that are emerging in scholarly prowess.Rankings of individual scholars worldwide are first generated using the recently introduced,fully indexed ScholarGPS database.The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor,in 14 individual Fields,in 177 Disciplines,and in approximately 350,000 unique Specialties.Rankings associated with five specific Fields(Medicine,Engineering&Computer Science,Life Sciences,Physical Sciences&Mathematics,and Social Sciences),and in two Disciplines(Chemistry,and Electrical&Computer Engineering)are presented as examples,and changes in the rankings over time are discussed.Findings:For the Fields considered here,the Top 20 institutional rankings in Medicine have undergone the least change(lifetime versus last five years),while the rankings in Engineering&Computer Science have exhibited significant change.The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions,although this emergence is shown to be highly Field-and Discipline-dependent.Practical implementations:Existing rankings of academic institutions have:(i)often been restricted to pre-selected institutions,clouding the potential discovery of scholarly activity in emerging institutions and countries;(ii)considered only broad areas of research,limiting the ability of university leadership to act on the assessments in a concrete manner,or in contrast;(iii)have considered only a narrow area of research for comparison,diminishing the broader applicability and impact of the assessment.In general,existing institutional rankings depend on which institutions are included in the ranking process,which areas of research are considered,the breadth(or granularity)of the research areas of interest,and the methodologies used to define and quantify research performance.In contrast,the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall(all Fields)level,to the level of the Specialty.The methods may also assist identification of the root causes of shifts in institution rankings,and how these shifts vary across hundreds of thousands of Fields,Disciplines,and Specialties of scholarly endeavor.Originality/value:This study provides the first ranking of all academic institutions worldwide over Fields,Disciplines,and Specialties based on a unique methodology that quantifies the productivity,impact,and quality of individual scholars.展开更多
Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured...Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.展开更多
In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web page...In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.展开更多
随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使...随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使用二进制值来表示用户是否访问一个兴趣点;2)基于矩阵分解的兴趣点推荐算法把签到频率数据和传统推荐系统中的评分数据等同看待,使用高斯分布模型建模用户的签到行为;3)忽视用户签到数据的隐式反馈属性.为解决以上问题,提出一个基于Ranking的泊松矩阵分解兴趣点推荐算法.首先,根据LBSN中用户的签到行为特点,利用泊松分布模型替代高斯分布模型建模用户在兴趣点上签到行为;然后采用BPR(Bayesian personalized ranking)标准优化泊松矩阵分解的损失函数,拟合用户在兴趣点对上的偏序关系;最后,利用包含地域影响力的正则化因子约束泊松矩阵分解的过程.在真实数据集上的实验结果表明:基于Ranking的泊松矩阵分解兴趣点推荐算法的性能优于传统的兴趣点推荐算法.展开更多
The presence of pesticide residues in pears is a serious health concern. This study presents the results from a 2-year investigation （2013-2014） that used gas chromatography, GS/MS and UPLC/MS-MS to measure the leve...The presence of pesticide residues in pears is a serious health concern. This study presents the results from a 2-year investigation （2013-2014） that used gas chromatography, GS/MS and UPLC/MS-MS to measure the levels of 104 pesti- cides in 310 pear samples. In 93.2% of the samples, 43 pesticides were detected, of which the maximum residue levels （MRLs） were exceeded in 2.6% of the samples. Multiple residues （two to eight compounds） were present in 69.7% of the samples; one sample contained nine pesticides and one sample contained 10. Only 6.8% of the samples did not contain residues. To assess the health risks, the pesticide residue data have been combined with daily pear consumption data for children and adult populations. A deterministic model was used to assess the chronic and acute exposures based on the Joint Meeting on Pesticide Residues （JMPR） method. A potential acute risk was demonstrated for children in the case of bifenthrin, which was found to be present at 105.36% of the acute reference dose （ARfD） value. The long- term exposure of the Chinese consumer to pesticide residues through the consumption of raw pears was far below the acceptable daily intake （ADI） criterion. Additionally, the matrix ranking scheme was used to classify risk subgroups of pesticides and pear samples. In general, 95.5% of samples were deemed to be safe and nine pesticides were classified as being of a relatively high risk. The findings indicated that the occurrence of pesticide residues in pears should not be considered a serious public health problem. Nevertheless, a more detailed study is required for vulnerable consumer groups, especially children. Continuous monitoring of pesticides in pears and tighter regulation of pesticide residue standards are recommended.展开更多
Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve...Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve the P management strategies in Guishui River watershed are important for the safety of drinking water in this region. In this study, a Revised Field P Ranking Scheme （PRS） was developed to reflect the field vulnerability of P loss at the field scale based on the Field PRS. In this new scheme, six factors are included, and each one was assigned a relative weight and a determination method. The affecting factors were classified into transport factors and source factors, and, the standards of environmental quality on surface water and soil erosion classification and degradation of the China were used in this scheme. By the new scheme, thirty-four fields in the Guishui River were categorized as ＂low＂, ＂medium＂ or ＂high＂ potential for P loss into the runoff. The results showed that the P loss risks of orchard and vegetable fields were higher than that of corn and soybean fields. The source factors were the main factors to affect P loss from the study area. In the study area, controlling P input and improving P usage efficiency are critical to decrease P loss. Based on the results, it was suggested that more attention should be paid on the fields of vegetable and orchard since they have extremely high usage rate of P and high soil test of P. Compared with P surplus by field measurements, the Revised Field PRS was more suitable for reflecting the characteristics of fields, and had higher potential capacity to identify critical source areas of P loss than PRS.展开更多
Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical metho...Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical methods,including joint regression analysis(JRA),additive mean effects and multiplicative interaction(AMMI)analysis,genotype plus GE interaction(GGE)biplot analysis,and yield–stability(YSi)statistic were used to evaluate GE interaction in20 winter wheat genotypes grown in 24 environments in Iran.The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield,stability and yield–stability.Three kinds of genotypic ranks(yield ranks,stability ranks,and yield–stability ranks)were determined with each method.The results indicated the presence of GE interaction,suggesting the need for stability analysis.With respect to yield,the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated(P<0.01).For stability ranking,the rank correlations ranged from 0.53(GGE–YSi;P<0.05)to0.97(JRA–YSi;P<0.01).AMMI distance(AMMID)was highly correlated(P<0.01)with variance of regression deviation(S2di)in JRA(r=0.83)and Shukla stability variance(σ2)in YSi(r=0.86),indicating that these stability indices can be used interchangeably.No correlation was found between yield ranks and stability ranks(AMMID,S2di,σ2,and GGE stability index),indicating that they measure static stability and accordingly could be used if selection is based primarily on stability.For yield–stability,rank correlation coefficients among the statistical methods varied from 0.64(JRA–YSi;P<0.01)to 0.89(AMMI–YSi;P<0.01),indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance.Based on the results,it can be concluded that YSi was closely correlated with(i)JRA in ranking genotypes for stability and(ii)AMMI for integrating yield and stability.展开更多
文摘Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and quality.Design/methodology/approach:The institutional ranking process developed here considers all institutions in all countries and regions,thereby including those that are established,as well as those that are emerging in scholarly prowess.Rankings of individual scholars worldwide are first generated using the recently introduced,fully indexed ScholarGPS database.The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor,in 14 individual Fields,in 177 Disciplines,and in approximately 350,000 unique Specialties.Rankings associated with five specific Fields(Medicine,Engineering&Computer Science,Life Sciences,Physical Sciences&Mathematics,and Social Sciences),and in two Disciplines(Chemistry,and Electrical&Computer Engineering)are presented as examples,and changes in the rankings over time are discussed.Findings:For the Fields considered here,the Top 20 institutional rankings in Medicine have undergone the least change(lifetime versus last five years),while the rankings in Engineering&Computer Science have exhibited significant change.The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions,although this emergence is shown to be highly Field-and Discipline-dependent.Practical implementations:Existing rankings of academic institutions have:(i)often been restricted to pre-selected institutions,clouding the potential discovery of scholarly activity in emerging institutions and countries;(ii)considered only broad areas of research,limiting the ability of university leadership to act on the assessments in a concrete manner,or in contrast;(iii)have considered only a narrow area of research for comparison,diminishing the broader applicability and impact of the assessment.In general,existing institutional rankings depend on which institutions are included in the ranking process,which areas of research are considered,the breadth(or granularity)of the research areas of interest,and the methodologies used to define and quantify research performance.In contrast,the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall(all Fields)level,to the level of the Specialty.The methods may also assist identification of the root causes of shifts in institution rankings,and how these shifts vary across hundreds of thousands of Fields,Disciplines,and Specialties of scholarly endeavor.Originality/value:This study provides the first ranking of all academic institutions worldwide over Fields,Disciplines,and Specialties based on a unique methodology that quantifies the productivity,impact,and quality of individual scholars.
基金supported by the National Natural Science Foundation of China(71690233,71901214)。
文摘Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.
基金The Natural Science Foundation of South-Central University for Nationalities(No.YZZ07006)
文摘In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.
文摘随着基于位置社交网络(location-based social network,LBSN)的发展,兴趣点推荐成为满足用户个性化需求、减轻信息过载问题的重要手段.然而,已有的兴趣点推荐算法存在如下的问题:1)多数已有的兴趣点推荐算法简化用户签到频率数据,仅使用二进制值来表示用户是否访问一个兴趣点;2)基于矩阵分解的兴趣点推荐算法把签到频率数据和传统推荐系统中的评分数据等同看待,使用高斯分布模型建模用户的签到行为;3)忽视用户签到数据的隐式反馈属性.为解决以上问题,提出一个基于Ranking的泊松矩阵分解兴趣点推荐算法.首先,根据LBSN中用户的签到行为特点,利用泊松分布模型替代高斯分布模型建模用户在兴趣点上签到行为;然后采用BPR(Bayesian personalized ranking)标准优化泊松矩阵分解的损失函数,拟合用户在兴趣点对上的偏序关系;最后,利用包含地域影响力的正则化因子约束泊松矩阵分解的过程.在真实数据集上的实验结果表明:基于Ranking的泊松矩阵分解兴趣点推荐算法的性能优于传统的兴趣点推荐算法.
基金financially supported by the National Program for Quality and Safety Risk Assessment of Agricultural Products of China (GJFP2014002, GJFP2015002)the Core Research Budget of the Non-Profit Governmental Research Institution of China (0032014013)
文摘The presence of pesticide residues in pears is a serious health concern. This study presents the results from a 2-year investigation （2013-2014） that used gas chromatography, GS/MS and UPLC/MS-MS to measure the levels of 104 pesti- cides in 310 pear samples. In 93.2% of the samples, 43 pesticides were detected, of which the maximum residue levels （MRLs） were exceeded in 2.6% of the samples. Multiple residues （two to eight compounds） were present in 69.7% of the samples; one sample contained nine pesticides and one sample contained 10. Only 6.8% of the samples did not contain residues. To assess the health risks, the pesticide residue data have been combined with daily pear consumption data for children and adult populations. A deterministic model was used to assess the chronic and acute exposures based on the Joint Meeting on Pesticide Residues （JMPR） method. A potential acute risk was demonstrated for children in the case of bifenthrin, which was found to be present at 105.36% of the acute reference dose （ARfD） value. The long- term exposure of the Chinese consumer to pesticide residues through the consumption of raw pears was far below the acceptable daily intake （ADI） criterion. Additionally, the matrix ranking scheme was used to classify risk subgroups of pesticides and pear samples. In general, 95.5% of samples were deemed to be safe and nine pesticides were classified as being of a relatively high risk. The findings indicated that the occurrence of pesticide residues in pears should not be considered a serious public health problem. Nevertheless, a more detailed study is required for vulnerable consumer groups, especially children. Continuous monitoring of pesticides in pears and tighter regulation of pesticide residue standards are recommended.
基金Project supported by the National Basic Research Program of China(No.2005CB121107)the Innovation Research Group of National Basic Research Program of China(No.2005).
文摘Guanting Reservoir, one of the drinking water supply sources of Beijing, suffers from water eutrophication. It is mainly supplied by Guishui River. Thus, to investigate the reasons of phosphorus （P） loss and improve the P management strategies in Guishui River watershed are important for the safety of drinking water in this region. In this study, a Revised Field P Ranking Scheme （PRS） was developed to reflect the field vulnerability of P loss at the field scale based on the Field PRS. In this new scheme, six factors are included, and each one was assigned a relative weight and a determination method. The affecting factors were classified into transport factors and source factors, and, the standards of environmental quality on surface water and soil erosion classification and degradation of the China were used in this scheme. By the new scheme, thirty-four fields in the Guishui River were categorized as ＂low＂, ＂medium＂ or ＂high＂ potential for P loss into the runoff. The results showed that the P loss risks of orchard and vegetable fields were higher than that of corn and soybean fields. The source factors were the main factors to affect P loss from the study area. In the study area, controlling P input and improving P usage efficiency are critical to decrease P loss. Based on the results, it was suggested that more attention should be paid on the fields of vegetable and orchard since they have extremely high usage rate of P and high soil test of P. Compared with P surplus by field measurements, the Revised Field PRS was more suitable for reflecting the characteristics of fields, and had higher potential capacity to identify critical source areas of P loss than PRS.
基金the bread wheat project of the Dryland Agricultural Research Institute (DARI)supported by the Agricultural Research and Education Organization (AREO) of Iran
文摘Several statistical methods have been developed for analyzing genotype×environment(GE)interactions in crop breeding programs to identify genotypes with high yield and stability performances.Four statistical methods,including joint regression analysis(JRA),additive mean effects and multiplicative interaction(AMMI)analysis,genotype plus GE interaction(GGE)biplot analysis,and yield–stability(YSi)statistic were used to evaluate GE interaction in20 winter wheat genotypes grown in 24 environments in Iran.The main objective was to evaluate the rank correlations among the four statistical methods in genotype rankings for yield,stability and yield–stability.Three kinds of genotypic ranks(yield ranks,stability ranks,and yield–stability ranks)were determined with each method.The results indicated the presence of GE interaction,suggesting the need for stability analysis.With respect to yield,the genotype rankings by the GGE biplot and AMMI analysis were significantly correlated(P<0.01).For stability ranking,the rank correlations ranged from 0.53(GGE–YSi;P<0.05)to0.97(JRA–YSi;P<0.01).AMMI distance(AMMID)was highly correlated(P<0.01)with variance of regression deviation(S2di)in JRA(r=0.83)and Shukla stability variance(σ2)in YSi(r=0.86),indicating that these stability indices can be used interchangeably.No correlation was found between yield ranks and stability ranks(AMMID,S2di,σ2,and GGE stability index),indicating that they measure static stability and accordingly could be used if selection is based primarily on stability.For yield–stability,rank correlation coefficients among the statistical methods varied from 0.64(JRA–YSi;P<0.01)to 0.89(AMMI–YSi;P<0.01),indicating that AMMI and YSi were closely associated in the genotype ranking for integrating yield with stability performance.Based on the results,it can be concluded that YSi was closely correlated with(i)JRA in ranking genotypes for stability and(ii)AMMI for integrating yield and stability.
