The article deals with possible approaches to the management of manufacturing organizations. The authors emphasize the need for integration of lean management with eco-innovation. This integration represents a sustain...The article deals with possible approaches to the management of manufacturing organizations. The authors emphasize the need for integration of lean management with eco-innovation. This integration represents a sustainable development so that environmental impacts are reduced, more effective use of natural resources is achieved and production costs are reduced. Manufacturing organizations based on that approach must use so-called pull production control systems. Pull systems are most often presented in production management system--Kanban. This article also deals with specification of these systems and the development of pull strategies in production management in order to increase efficiency of manufacturing enterprise.展开更多
Green technology innovation meets the dual expectation of innovative development and green development perspectives.Under the canonical demand-pull and policy-push theories,a long-term mechanism for green technology i...Green technology innovation meets the dual expectation of innovative development and green development perspectives.Under the canonical demand-pull and policy-push theories,a long-term mechanism for green technology innovation could be formed through upstream policy push and downstream demand-pull.Leveraging China's regional carbon emission trading scheme pilots as a quasi-natural experiment,this paper examines the policy-push and demand-pull effects on innovation in renewable energy patents.The data pertain to the city-level renewable energy patents from 2000 to 2020.Based upon the triple difference-in-difference method,results suggest that both policy-push and demandpull factors exert positive effects on innovation.This paper further explores the practical and theoretical implications of green technology innovation under the new development perspective.展开更多
As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request ...As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the deficiency.In this paper,an approach CTCPPre is proposed to predict the accepted pull requests in GitHub.CTCPPre mainly considers code features of modified changes,text features of pull requests’description,contributor features of developers’previous behaviors,and project features of development environment.The effectiveness of CTCPPre on 28 projects containing 221096 pull requests is evaluated.Experimental results show that CTCPPre has good performances by achieving accuracy of 0.82,AUC of 0.76 and F1-score of 0.88 on average.It is compared with the state of art accepted pull request prediction approach RFPredict.On average across 28 projects,CTCPPre outperforms RFPredict by 6.64%,16.06%and 4.79%in terms of accuracy,AUC and F1-score,respectively.展开更多
文摘The article deals with possible approaches to the management of manufacturing organizations. The authors emphasize the need for integration of lean management with eco-innovation. This integration represents a sustainable development so that environmental impacts are reduced, more effective use of natural resources is achieved and production costs are reduced. Manufacturing organizations based on that approach must use so-called pull production control systems. Pull systems are most often presented in production management system--Kanban. This article also deals with specification of these systems and the development of pull strategies in production management in order to increase efficiency of manufacturing enterprise.
基金sponsored by the project “Economic and Environmental Assessment of Carbon Emission Trading Scheme:Theory and Evidence from China Firm-Level Data”(No. 72073055) of the National Natural Science Foundation of ChinaQinglan Project of Jiangsu Province
文摘Green technology innovation meets the dual expectation of innovative development and green development perspectives.Under the canonical demand-pull and policy-push theories,a long-term mechanism for green technology innovation could be formed through upstream policy push and downstream demand-pull.Leveraging China's regional carbon emission trading scheme pilots as a quasi-natural experiment,this paper examines the policy-push and demand-pull effects on innovation in renewable energy patents.The data pertain to the city-level renewable energy patents from 2000 to 2020.Based upon the triple difference-in-difference method,results suggest that both policy-push and demandpull factors exert positive effects on innovation.This paper further explores the practical and theoretical implications of green technology innovation under the new development perspective.
基金Project(2018YFB1004202)supported by the National Key Research and Development Program of ChinaProject(61732019)supported by the National Natural Science Foundation of ChinaProject(SKLSDE-2018ZX-06)supported by the State Key Laboratory of Software Development Environment,China
文摘As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull requests.An accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the deficiency.In this paper,an approach CTCPPre is proposed to predict the accepted pull requests in GitHub.CTCPPre mainly considers code features of modified changes,text features of pull requests’description,contributor features of developers’previous behaviors,and project features of development environment.The effectiveness of CTCPPre on 28 projects containing 221096 pull requests is evaluated.Experimental results show that CTCPPre has good performances by achieving accuracy of 0.82,AUC of 0.76 and F1-score of 0.88 on average.It is compared with the state of art accepted pull request prediction approach RFPredict.On average across 28 projects,CTCPPre outperforms RFPredict by 6.64%,16.06%and 4.79%in terms of accuracy,AUC and F1-score,respectively.
