Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which cont...Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which contradicts its prominent scientific standing.The purpose of this study is to investigate the underlying causes of such inaccurate assessments and to propose methods for conducting more reliable evaluations.Design/methodology/approach:The study evaluates the effectiveness of top-percentile citation metrics as indicators of breakthrough research.Using case studies of selected countries and research topics,the study examines how deviations from lognormal citation distributions impact the accuracy of these percentile indicators.A similar analysis is conducted using university data from the Leiden Ranking to investigate citation distribution deviations at the institutional level.Findings:The study finds that inflated lower tails in citation distributions lead to undervaluation of research capabilities in advanced technological countries,as captured by some percentile indicators.Conversely,research-intensive universities exhibit the opposite trend:a reduced lower tail relative to the upper tail,which causes percentile indicators to overestimate their actual research capacity.Research limitations:The descriptions are mathematical facts that are self-evident.Practical implications:The ratios between the number of papers in the global top 10%and 1%by citation count to the total number of papers are commonly used to describe research performance.However,due to variations in citation patterns across countries and institutions with reference to the global pattern,these ratios can be misleading and lose their value as research indicators.Originality/value:Size-independent research performance indicators,obtained as the ratios between paper counts in top percentiles and the total numbers of publications,are widely used by public and private institutions.This study demonstrates that the use of these ratios for research evaluations and country rankings can be highly misleading.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
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
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
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 paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial ra...This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.展开更多
While academics and university administrators often criticize rankings,league tables have become important tools for student decision-making,especially in the Chinese sector.Yet,research has not fully explored how stu...While academics and university administrators often criticize rankings,league tables have become important tools for student decision-making,especially in the Chinese sector.Yet,research has not fully explored how students in China have engaged with both global and local rankings,as most studies have focused on one setting or the other.Likewise,researchers have not tested students’knowledge of rankings,despite the intense focus on these actors by universities.Using a survey of over 900 students from Chinese universities,the author explored how knowledge of rankings varies in different student populations.Through multivariate analysis,it is found that students from elite institutions and those with educated parents were more attuned to university rankings in general.However,when testing students’knowledge of rankings,elite university students performed better in knowing their domestic ranking,but worse when guessing their global ranking,while associations to parental education disappeared.This study,the first of its kind in terms of testing student knowledge,illustrates that the impact from university rankings are mitigated by local and individual characteristics.展开更多
Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation gener...Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with ...Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.展开更多
Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua Univers...Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua University to rank No.1 on the latest list of Chinese college rankings.The rankings are an important part of the book Picking Your University展开更多
文摘Purpose:Citation-based assessments of countries’research capabilities often misrepresent their ability to achieve breakthrough advancements.These assessments commonly classify Japan as a developing country,which contradicts its prominent scientific standing.The purpose of this study is to investigate the underlying causes of such inaccurate assessments and to propose methods for conducting more reliable evaluations.Design/methodology/approach:The study evaluates the effectiveness of top-percentile citation metrics as indicators of breakthrough research.Using case studies of selected countries and research topics,the study examines how deviations from lognormal citation distributions impact the accuracy of these percentile indicators.A similar analysis is conducted using university data from the Leiden Ranking to investigate citation distribution deviations at the institutional level.Findings:The study finds that inflated lower tails in citation distributions lead to undervaluation of research capabilities in advanced technological countries,as captured by some percentile indicators.Conversely,research-intensive universities exhibit the opposite trend:a reduced lower tail relative to the upper tail,which causes percentile indicators to overestimate their actual research capacity.Research limitations:The descriptions are mathematical facts that are self-evident.Practical implications:The ratios between the number of papers in the global top 10%and 1%by citation count to the total number of papers are commonly used to describe research performance.However,due to variations in citation patterns across countries and institutions with reference to the global pattern,these ratios can be misleading and lose their value as research indicators.Originality/value:Size-independent research performance indicators,obtained as the ratios between paper counts in top percentiles and the total numbers of publications,are widely used by public and private institutions.This study demonstrates that the use of these ratios for research evaluations and country rankings can be highly misleading.
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.
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.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(No.ZCLQN25A0103)。
文摘This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.
文摘目的基于“脑-肠-骨轴”初步探讨左归丸对老年性骨质疏松症(senile osteoporosis,SOP)模型小鼠海马区神经元退行性病变、肠道菌群变化及骨量丢失的作用。方法连续12周腹腔注射D-半乳糖(120 mg/kg)构建SOP模型,将小鼠随机分为空白组、模型组、左归丸高剂量组、左归丸低剂量组;予以左归丸药物干预8周,随后进行Morris水迷宫实验检测小鼠认知功能。取材后检测各组脑组织氧化应激指标,尼氏染色法检测海马尼式神经元完整性,16 S rRNA检测肠道菌群多样性,小动物X光机检测小鼠骨量丢失情况,小鼠血清检测骨代谢指标,免疫组织化学法检测骨代谢通路相关蛋白,采用spearman分析法对血清骨代谢因子、脑组织氧化应激因子与肠道菌群中的差异菌群进行关联分析。结果与Model组相比,左归丸给药组可显著缩短逃避潜伏期时间(P<0.05);显著增加海马区尼式小体数量(P<0.01);提高脑组织抗氧化酶含量(P<0.05);在门水平上,Model组小鼠肠道菌群中Bacteroidota相对丰度显著增高(P<0.01),左归丸给药组Verrucomicrobiota等菌群相对丰度显著升高(P<0.05),Bacteroidota相对丰度显著下降(P<0.05)。小鼠股骨干骺端与骨干区的骨量丢失得到改善,显著改善血清骨代谢指标(P<0.01),显著提高OPG/RANKL蛋白含量比值(P<0.01)。相关性分析显示,过氧化氢酶与肠道菌群中的Proteobacteria呈正相关(P<0.01),与Prevotellaceae_NK3B31_group呈负相关(P<0.01)。结论左归丸依据“脑-肠-骨轴”改善SOP小鼠肠道菌群丰度,进而缓解海马神经元退行性病变,调节OPG/RANK/RANKL通路缓解骨量丢失。
文摘While academics and university administrators often criticize rankings,league tables have become important tools for student decision-making,especially in the Chinese sector.Yet,research has not fully explored how students in China have engaged with both global and local rankings,as most studies have focused on one setting or the other.Likewise,researchers have not tested students’knowledge of rankings,despite the intense focus on these actors by universities.Using a survey of over 900 students from Chinese universities,the author explored how knowledge of rankings varies in different student populations.Through multivariate analysis,it is found that students from elite institutions and those with educated parents were more attuned to university rankings in general.However,when testing students’knowledge of rankings,elite university students performed better in knowing their domestic ranking,but worse when guessing their global ranking,while associations to parental education disappeared.This study,the first of its kind in terms of testing student knowledge,illustrates that the impact from university rankings are mitigated by local and individual characteristics.
基金supported by the National Key Research and Development Program of China(2020AAA0109300)the Shanghai Collaborative Innovation Center of data intelligence technology(No.0232-A1-8900-24-13).
文摘Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
文摘Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.
文摘Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua University to rank No.1 on the latest list of Chinese college rankings.The rankings are an important part of the book Picking Your University
