Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, w...Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, while other countries use an automated coding system. The outcome of these two different methods in terms of the selected underlying cause of death for statistics may vary considerably. Therefore, this study explores the effect of coding method on the ranking of countries by major causes of death. Method: Age and sex standardized rates were extracted for 33 European (related) countries from the cause-of-death registry of the European Statistical Office (Eurostat). Wilcoxon’s rank sum test was applied to the ranking of countries by major causes of death. Results: Statistically significant differences due to coding method were identified for dementia, stroke and pneumonia. These differences could be explained by a different selection of dementia or pneumonia as underlying cause of death and by a different certification practice for stroke. Conclusion: Coding method should be taken into account when constructing or interpreting rankings of countries by cause of death.展开更多
This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. ...This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. The purpose of this study was to evaluate the colleges on an efficiency basis rather than on a total score ranking scale as is the common practice of most publications that rank universities or programs. The ranking method used in this study is based on data envelopment analysis （DEA）, a nonparametric procedure for evaluating entities based upon examining inputs in relation to outputs achieved. The rankings using DEA were somewhat different than those given by Careers 360. The results of the DEA analysis of this study rank the universities that are the most efficient at getting students the best salaries and return on investment （ROI） based on the inputs of diversity, work experience, and residency. The authors conclude, as previous studies have shown, that DEA analysis is a useful and non-biased method of comparing university programs.展开更多
Purpose:The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special att...Purpose:The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special attention to their publication output.Design/methodology/approach:By using SciVal subject area data,we composed a formula describing the level of vulnerability of any given university in the case of losing any of its leading mentors,identifying other risk factors by dividing their careers into separate stages.Findings:It turns out that the higher field-weighed citation impact is,the better position universities reach in the rankings by subject and the vulnerability of institutions highly depends on the mentors,especially in view of their contribution to the topic clusters.Research limitations:The analysis covers the publication output of leading researchers working at four Hungarian universities,the scope of the analysis is worth being extended.Practical implications:Our analysis has the potential to give an applicable systemic approach as well as a data collection scheme to university managements so as to formulate an inclusive and comprehensive research strategy involving the introduction of a reward system aimed at publications and further encouraging national and international research cooperation.Originality/value:The methodology and the principles of risk assessment laid down in our paper are not restricted to measuring the vulnerability level of a limited group of academic institutions,they can be appropriately used for investigating the role of mentors or leading researchers at every university across the globe.展开更多
On Sep. 8,2009,Switzerland tops the overall ranking in The Global Competitiveness Report 2009-2010, released by the World Economic Forum ahead of its Annual Meeting of the New Champions 2009 in Dalian.
Purpose:Building on Leydesdorff,Bornmann,and Mingers(2019),we elaborate the differences between Tsinghua and Zhejiang University as an empirical example.We address the question of whether differences are statistically...Purpose:Building on Leydesdorff,Bornmann,and Mingers(2019),we elaborate the differences between Tsinghua and Zhejiang University as an empirical example.We address the question of whether differences are statistically significant in the rankings of Chinese universities.We propose methods for measuring statistical significance among different universities within or among countries.Design/methodology/approach:Based on z-testing and overlapping confidence intervals,and using data about 205 Chinese universities included in the Leiden Rankings 2020,we argue that three main groups of Chinese research universities can be distinguished(low,middle,and high).Findings:When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020,the results similarly indicate three main groups:low,middle,and high.Using this data(Leiden Rankings and Web of Science),the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap.Research limitations:We show empirically that differences in ranking may be due to changes in the data,the models,or the modeling effects on the data.The scientometric groupings are not always stable when we use different methods.Practical implications:Differences among universities can be tested for their statistical significance.The statistics relativize the values of decimals in the rankings.One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings.Originality/value:In the discussion about the rankings of universities,the question of whether differences are statistically significant,has,in our opinion,insufficiently been addressed in research evaluations.展开更多
Purpose: Study how economic parameters affect positions in the Academic Ranking of World Universities' top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with ...Purpose: Study how economic parameters affect positions in the Academic Ranking of World Universities' top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with listed higher education institutions. Design/methodology/approach: The methodology used capitalises on the multi-variate characteristics of the data analysed. The multi-colinearity problem posed is solved by running principal components prior to regression analysis, using both classical(OLS) and robust(Huber and Tukey) methods. Findings: Our results revealed that countries/regions with long ranking traditions are highly competitive. Findings also showed that some countries/regions such as Germany, United Kingdom, Canada, and Italy, had a larger number of universities in the top positions than predicted by the regression model. In contrast, for Japan, a country where social and economic performance is high, the number of ARWU universities projected by the model was much larger than the actual figure. In much the same vein, countries/regions that invest heavily in education, such as Japan and Denmark, had lower than expected results.Research limitations: Using data from only one ranking is a limitation of this study, but the methodology used could be useful to other global rankings. Practical implications: The results provide good insights for policy makers. They indicate the existence of a relationship between research output and the number of universities per million inhabitants. Countries/regions, which have historically prioritised higher education, exhibited highest values for indicators that compose the rankings methodology; furthermore,minimum increase in welfare indicators could exhibited significant rises in the presence of their universities on the rankings.Originality/value: This study is well defined and the result answers important questions about characteristics of countries/regions and their higher education system.展开更多
This paper will discuss one topic in the current debate on higher education： How power is exercised between universities？ How do colleges determine what the best college is？ What are the differences in the excersis...This paper will discuss one topic in the current debate on higher education： How power is exercised between universities？ How do colleges determine what the best college is？ What are the differences in the excersise of power in the digital age？ The authors analyze one of the mechanisms of relationship and contact between different universities： the rankings. They will discuss the practices that allow certain values and organizations they are becoming central nodes between universities and the influences of the information and communication technologies in the measurement mechanisms. The authors seek to show the rankings serve as mechanisms to exercise power among universities. These measurements become a tool and justification in competition between universities for resources such as funding, prestige, and student demand. The analysis is based on the University of Mexico, the authors use the ranking of the best universities in Latin America and the best universities in Mexico.展开更多
To explore the geological characteristics and exploration potential of the Carboniferous Benxi Formation coal rock gas in the Ordos Basin,this paper presents a systematic research on the coal rock distribution,coal ro...To explore the geological characteristics and exploration potential of the Carboniferous Benxi Formation coal rock gas in the Ordos Basin,this paper presents a systematic research on the coal rock distribution,coal rock reservoirs,coal rock quality,and coal rock gas features,resources and enrichment.Coal rock gas is a high-quality resource distinct from coalbed methane,and it has unique features in terms of burial depth,gas source,reservoir,gas content,and carbon isotopic composition.The Benxi Formation coal rocks cover an area of 16×104km^(2),with thicknesses ranging from 2 m to 25 m,primarily consisting of bright and semi-bright coals with primitive structures and low volatile and ash contents,indicating a good coal quality.The medium-to-high rank coal rocks have the total organic carbon(TOC)content ranging from 33.49%to 86.11%,averaging75.16%.They have a high degree of thermal evolution(Roof 1.2%-2.8%),and a high gas-generating capacity.They also have high stable carbon isotopic values(δ13C1of-37.6‰to-16‰;δ13C2of-21.7‰to-14.3‰).Deep coal rocks develop matrix pores such as gas bubble pores,organic pores,and inorganic mineral pores,which,together with cleats and fractures,form good reservoir spaces.The coal rock reservoirs exhibit the porosity of 0.54%-10.67%(averaging 5.42%)and the permeability of(0.001-14.600)×10^(-3)μm^(2)(averaging 2.32×10^(-3)μm^(2)).Vertically,there are five types of coal rock gas accumulation and dissipation combinations,among which the coal rock-mudstone gas accumulation combination and the coal rock-limestone gas accumulation combination are the most important,with good sealing conditions and high peak values of total hydrocarbon in gas logging.A model of coal rock gas accumulation has been constructed,which includes widespread distribution of medium-to-high rank coal rocks continually generating gas,matrix pores and cleats/fractures in coal rocks acting as large-scale reservoir spaces,tight cap rocks providing sealing,source-reservoir integration,and five types of efficient enrichment patterns(lateral pinchout complex,lenses,low-amplitude structures,nose-like structures,and lithologically self-sealing).According to the geological characteristics of coal rock gas,the Benxi Formation is divided into 8 plays,and the estimated coal rock gas resources with a buried depth of more than 2000 m are more than 12.33×10^(12)m^(3).The above understandings guide the deployment of risk exploration.Two wells drilled accordingly obtained an industrial gas flow,driving the further deployment of exploratory and appraisal wells.Substantial breakthroughs have been achieved,with the possible reserves over a trillion cubic meters and the proved reserves over a hundred billion cubic meters,which is of great significance for the reserves increase and efficient development of natural gas in China.展开更多
Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices...Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.展开更多
文摘Background: Cause-of-death rankings are often used for planning or evaluating health policy measures. In the European Union, some countries produce cause-of-death statistics by a manual coding of death certificates, while other countries use an automated coding system. The outcome of these two different methods in terms of the selected underlying cause of death for statistics may vary considerably. Therefore, this study explores the effect of coding method on the ranking of countries by major causes of death. Method: Age and sex standardized rates were extracted for 33 European (related) countries from the cause-of-death registry of the European Statistical Office (Eurostat). Wilcoxon’s rank sum test was applied to the ranking of countries by major causes of death. Results: Statistically significant differences due to coding method were identified for dementia, stroke and pneumonia. These differences could be explained by a different selection of dementia or pneumonia as underlying cause of death and by a different certification practice for stroke. Conclusion: Coding method should be taken into account when constructing or interpreting rankings of countries by cause of death.
文摘This study examines the relative efficiency of the top 20 Indian public colleges that offer MBAs. These colleges were chosen from a list provided by Careers 360, a magazine in India known for its university rankings. The purpose of this study was to evaluate the colleges on an efficiency basis rather than on a total score ranking scale as is the common practice of most publications that rank universities or programs. The ranking method used in this study is based on data envelopment analysis （DEA）, a nonparametric procedure for evaluating entities based upon examining inputs in relation to outputs achieved. The rankings using DEA were somewhat different than those given by Careers 360. The results of the DEA analysis of this study rank the universities that are the most efficient at getting students the best salaries and return on investment （ROI） based on the inputs of diversity, work experience, and residency. The authors conclude, as previous studies have shown, that DEA analysis is a useful and non-biased method of comparing university programs.
文摘Purpose:The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special attention to their publication output.Design/methodology/approach:By using SciVal subject area data,we composed a formula describing the level of vulnerability of any given university in the case of losing any of its leading mentors,identifying other risk factors by dividing their careers into separate stages.Findings:It turns out that the higher field-weighed citation impact is,the better position universities reach in the rankings by subject and the vulnerability of institutions highly depends on the mentors,especially in view of their contribution to the topic clusters.Research limitations:The analysis covers the publication output of leading researchers working at four Hungarian universities,the scope of the analysis is worth being extended.Practical implications:Our analysis has the potential to give an applicable systemic approach as well as a data collection scheme to university managements so as to formulate an inclusive and comprehensive research strategy involving the introduction of a reward system aimed at publications and further encouraging national and international research cooperation.Originality/value:The methodology and the principles of risk assessment laid down in our paper are not restricted to measuring the vulnerability level of a limited group of academic institutions,they can be appropriately used for investigating the role of mentors or leading researchers at every university across the globe.
文摘On Sep. 8,2009,Switzerland tops the overall ranking in The Global Competitiveness Report 2009-2010, released by the World Economic Forum ahead of its Annual Meeting of the New Champions 2009 in Dalian.
基金the National Natural Science Foundation of China(Grant No.71974150,71573085)。
文摘Purpose:Building on Leydesdorff,Bornmann,and Mingers(2019),we elaborate the differences between Tsinghua and Zhejiang University as an empirical example.We address the question of whether differences are statistically significant in the rankings of Chinese universities.We propose methods for measuring statistical significance among different universities within or among countries.Design/methodology/approach:Based on z-testing and overlapping confidence intervals,and using data about 205 Chinese universities included in the Leiden Rankings 2020,we argue that three main groups of Chinese research universities can be distinguished(low,middle,and high).Findings:When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020,the results similarly indicate three main groups:low,middle,and high.Using this data(Leiden Rankings and Web of Science),the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap.Research limitations:We show empirically that differences in ranking may be due to changes in the data,the models,or the modeling effects on the data.The scientometric groupings are not always stable when we use different methods.Practical implications:Differences among universities can be tested for their statistical significance.The statistics relativize the values of decimals in the rankings.One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings.Originality/value:In the discussion about the rankings of universities,the question of whether differences are statistically significant,has,in our opinion,insufficiently been addressed in research evaluations.
基金funded by CAPES (Coordinacao de Aperfeicoamento do Ensino) grant N. BEX 8354/13-8 awarded to Esteban Fernández Tuesta
文摘Purpose: Study how economic parameters affect positions in the Academic Ranking of World Universities' top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with listed higher education institutions. Design/methodology/approach: The methodology used capitalises on the multi-variate characteristics of the data analysed. The multi-colinearity problem posed is solved by running principal components prior to regression analysis, using both classical(OLS) and robust(Huber and Tukey) methods. Findings: Our results revealed that countries/regions with long ranking traditions are highly competitive. Findings also showed that some countries/regions such as Germany, United Kingdom, Canada, and Italy, had a larger number of universities in the top positions than predicted by the regression model. In contrast, for Japan, a country where social and economic performance is high, the number of ARWU universities projected by the model was much larger than the actual figure. In much the same vein, countries/regions that invest heavily in education, such as Japan and Denmark, had lower than expected results.Research limitations: Using data from only one ranking is a limitation of this study, but the methodology used could be useful to other global rankings. Practical implications: The results provide good insights for policy makers. They indicate the existence of a relationship between research output and the number of universities per million inhabitants. Countries/regions, which have historically prioritised higher education, exhibited highest values for indicators that compose the rankings methodology; furthermore,minimum increase in welfare indicators could exhibited significant rises in the presence of their universities on the rankings.Originality/value: This study is well defined and the result answers important questions about characteristics of countries/regions and their higher education system.
文摘This paper will discuss one topic in the current debate on higher education： How power is exercised between universities？ How do colleges determine what the best college is？ What are the differences in the excersise of power in the digital age？ The authors analyze one of the mechanisms of relationship and contact between different universities： the rankings. They will discuss the practices that allow certain values and organizations they are becoming central nodes between universities and the influences of the information and communication technologies in the measurement mechanisms. The authors seek to show the rankings serve as mechanisms to exercise power among universities. These measurements become a tool and justification in competition between universities for resources such as funding, prestige, and student demand. The analysis is based on the University of Mexico, the authors use the ranking of the best universities in Latin America and the best universities in Mexico.
基金Supported by the PetroChina Science and Technology Major Project(2023ZZ18-03)Changqing Oilfield Major Science and Technology Project(2023DZZ01)。
文摘To explore the geological characteristics and exploration potential of the Carboniferous Benxi Formation coal rock gas in the Ordos Basin,this paper presents a systematic research on the coal rock distribution,coal rock reservoirs,coal rock quality,and coal rock gas features,resources and enrichment.Coal rock gas is a high-quality resource distinct from coalbed methane,and it has unique features in terms of burial depth,gas source,reservoir,gas content,and carbon isotopic composition.The Benxi Formation coal rocks cover an area of 16×104km^(2),with thicknesses ranging from 2 m to 25 m,primarily consisting of bright and semi-bright coals with primitive structures and low volatile and ash contents,indicating a good coal quality.The medium-to-high rank coal rocks have the total organic carbon(TOC)content ranging from 33.49%to 86.11%,averaging75.16%.They have a high degree of thermal evolution(Roof 1.2%-2.8%),and a high gas-generating capacity.They also have high stable carbon isotopic values(δ13C1of-37.6‰to-16‰;δ13C2of-21.7‰to-14.3‰).Deep coal rocks develop matrix pores such as gas bubble pores,organic pores,and inorganic mineral pores,which,together with cleats and fractures,form good reservoir spaces.The coal rock reservoirs exhibit the porosity of 0.54%-10.67%(averaging 5.42%)and the permeability of(0.001-14.600)×10^(-3)μm^(2)(averaging 2.32×10^(-3)μm^(2)).Vertically,there are five types of coal rock gas accumulation and dissipation combinations,among which the coal rock-mudstone gas accumulation combination and the coal rock-limestone gas accumulation combination are the most important,with good sealing conditions and high peak values of total hydrocarbon in gas logging.A model of coal rock gas accumulation has been constructed,which includes widespread distribution of medium-to-high rank coal rocks continually generating gas,matrix pores and cleats/fractures in coal rocks acting as large-scale reservoir spaces,tight cap rocks providing sealing,source-reservoir integration,and five types of efficient enrichment patterns(lateral pinchout complex,lenses,low-amplitude structures,nose-like structures,and lithologically self-sealing).According to the geological characteristics of coal rock gas,the Benxi Formation is divided into 8 plays,and the estimated coal rock gas resources with a buried depth of more than 2000 m are more than 12.33×10^(12)m^(3).The above understandings guide the deployment of risk exploration.Two wells drilled accordingly obtained an industrial gas flow,driving the further deployment of exploratory and appraisal wells.Substantial breakthroughs have been achieved,with the possible reserves over a trillion cubic meters and the proved reserves over a hundred billion cubic meters,which is of great significance for the reserves increase and efficient development of natural gas in China.
基金supported by the National Natural Science Foundation of China(62171088,U19A2052,62020106011)the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China(ZYGX2021YGLH215,ZYGX2022YGRH005)。
文摘Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.
