This study aims to explore the transformative role of Artificial Intelligence(AI)in deepening Industry-Education Integration(IEI).Facing the rapidly changing skill demands of the AI era,tra-ditional IEI models are bot...This study aims to explore the transformative role of Artificial Intelligence(AI)in deepening Industry-Education Integration(IEI).Facing the rapidly changing skill demands of the AI era,tra-ditional IEI models are bottlenecked by information asymmetry and slow responsiveness.This paper introduces the“Catalytic Mechanism”perspective,arguing that AI is not merely an efficiency tool but rather a force that reshapes the static partnership between industry and education into a self-optimizing,symbiotic ecosystem by reducing transaction costs and accelerating system responsive-ness.This paper constructs a theoretical framework comprising three pillars:(1)AI-based dynamic skills mapping and curriculum reconstruction;(2)personalized learning pathways and career navi-gation;and(3)an intelligent collaborative ecosystem and value co-creation.The explanatory power of this framework is validated through a comparative case analysis of a university-led regional model and a corporate-led global model.The study finds that AI effectively overcomes the structural bar-riers of traditional models.Concurrently,it identifies challenges brought by AI integration,such as data privacy and algorithmic bias,and provides governance references for building a human-centric,AI-driven new paradigm for IEI,combining Chinese contexts with transnational comparative per-spectives.展开更多
Concentrations of heavy metals in 74 sediment samples from the Fenghe River, which originates from the north of the Qinling Mountains and flows through Xi'an, Shaanxi Province, China, were characterized by employi...Concentrations of heavy metals in 74 sediment samples from the Fenghe River, which originates from the north of the Qinling Mountains and flows through Xi'an, Shaanxi Province, China, were characterized by employing geographic information system(GIS)mapping, fuzzy synthetic assessment, and multivariate statistical analysis to determine the enrichment characteristics of heavy metals as well as their potential risks of pollution to sediments. Al, Cd, and Co were the major pollutants, with a high enrichment factor(EF) value. Heavy metal concentrations from samples near the paper plant were maintained at a high level. Significant enrichment of Al, Ba, Cr, Ni, Pb, and Co was found in the midstream and downstream, while high concentration of Cu occurred in the headwater stream. Based on the cluster and principal component analyses, sediment metals mainly came from the paper plants, agronomic practices, natural sources, and tourism, with a contribution of 51.59%, 23.01%, 14.21%, and 9.88%, respectively. Sediment pollution assessment explored using fuzzy theory based on the entropy method and toxicity coefficient showed that 26, 32, and 11 sites fell into Class III(slightly polluted), Class IV(moderately polluted), and Class V(heavily polluted), respectively, and their scores of membership degree in the polluted level were on the rise, suggesting a relatively high degree of sediment metal pollution in the study area. Closely related to the excessive industrial and agricultural applications, metal pollution in sediment is necessary to be addressed in the Fenghe River.展开更多
Prior research has consistently demonstrated that urban economic and social systems adhere to the empirical scaling law.Furthermore,a plethora of evidence,including the scale-free networks of energy metabolism,the all...Prior research has consistently demonstrated that urban economic and social systems adhere to the empirical scaling law.Furthermore,a plethora of evidence,including the scale-free networks of energy metabolism,the allometric growth patterns of species and populations,and the scaling law relationship between exergy and transformity in biosphere systems across various levels,indicates that urban ecosystems exhibit multi-level scaling law characteristics in energy metabolism under self-organization,alongside significant human activity imprints.This study synthesizes these findings to hypothesize that urban ecological components are also aligned with system-level scaling theory within the urban metabolism framework.This encompasses:1)the existence of multistable coexistence and mutual transformation phenomena,mirroring the dynamic nature of scaling laws;and 2)a nuanced balance between the ecosystem and the socio-economic system,particularly in the realms of spatial competition and output efficiency.The ecosystem scaling theory hypotheses of urban metabolic processes offer a theoretical foundation for identifying ecological security tipping points,which are pivotal in the strategic decision-making for ecological planning and management in the future.展开更多
文摘This study aims to explore the transformative role of Artificial Intelligence(AI)in deepening Industry-Education Integration(IEI).Facing the rapidly changing skill demands of the AI era,tra-ditional IEI models are bottlenecked by information asymmetry and slow responsiveness.This paper introduces the“Catalytic Mechanism”perspective,arguing that AI is not merely an efficiency tool but rather a force that reshapes the static partnership between industry and education into a self-optimizing,symbiotic ecosystem by reducing transaction costs and accelerating system responsive-ness.This paper constructs a theoretical framework comprising three pillars:(1)AI-based dynamic skills mapping and curriculum reconstruction;(2)personalized learning pathways and career navi-gation;and(3)an intelligent collaborative ecosystem and value co-creation.The explanatory power of this framework is validated through a comparative case analysis of a university-led regional model and a corporate-led global model.The study finds that AI effectively overcomes the structural bar-riers of traditional models.Concurrently,it identifies challenges brought by AI integration,such as data privacy and algorithmic bias,and provides governance references for building a human-centric,AI-driven new paradigm for IEI,combining Chinese contexts with transnational comparative per-spectives.
基金supported by the National Natural Science Foundation of China(Nos.41030744 and 41173123)
文摘Concentrations of heavy metals in 74 sediment samples from the Fenghe River, which originates from the north of the Qinling Mountains and flows through Xi'an, Shaanxi Province, China, were characterized by employing geographic information system(GIS)mapping, fuzzy synthetic assessment, and multivariate statistical analysis to determine the enrichment characteristics of heavy metals as well as their potential risks of pollution to sediments. Al, Cd, and Co were the major pollutants, with a high enrichment factor(EF) value. Heavy metal concentrations from samples near the paper plant were maintained at a high level. Significant enrichment of Al, Ba, Cr, Ni, Pb, and Co was found in the midstream and downstream, while high concentration of Cu occurred in the headwater stream. Based on the cluster and principal component analyses, sediment metals mainly came from the paper plants, agronomic practices, natural sources, and tourism, with a contribution of 51.59%, 23.01%, 14.21%, and 9.88%, respectively. Sediment pollution assessment explored using fuzzy theory based on the entropy method and toxicity coefficient showed that 26, 32, and 11 sites fell into Class III(slightly polluted), Class IV(moderately polluted), and Class V(heavily polluted), respectively, and their scores of membership degree in the polluted level were on the rise, suggesting a relatively high degree of sediment metal pollution in the study area. Closely related to the excessive industrial and agricultural applications, metal pollution in sediment is necessary to be addressed in the Fenghe River.
基金supported by the Key Projects of National Natural Science Foundation of China(No.52430003)the National Natural Science Foundation of China(No.52481540200)the Fundamental Research Funds for the Central Universities(China).Special thanks to the Young Talent Award committee of the FESE journal.
文摘Prior research has consistently demonstrated that urban economic and social systems adhere to the empirical scaling law.Furthermore,a plethora of evidence,including the scale-free networks of energy metabolism,the allometric growth patterns of species and populations,and the scaling law relationship between exergy and transformity in biosphere systems across various levels,indicates that urban ecosystems exhibit multi-level scaling law characteristics in energy metabolism under self-organization,alongside significant human activity imprints.This study synthesizes these findings to hypothesize that urban ecological components are also aligned with system-level scaling theory within the urban metabolism framework.This encompasses:1)the existence of multistable coexistence and mutual transformation phenomena,mirroring the dynamic nature of scaling laws;and 2)a nuanced balance between the ecosystem and the socio-economic system,particularly in the realms of spatial competition and output efficiency.The ecosystem scaling theory hypotheses of urban metabolic processes offer a theoretical foundation for identifying ecological security tipping points,which are pivotal in the strategic decision-making for ecological planning and management in the future.
