In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work invo...In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.展开更多
This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors....This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors.The GTFP in the carbon-intensive sectors experienced a decline between 2006 and 2011,followed by an upward trend beginning in 2012.Technological progress was the primary driver of GTFP growth,while business size was also a notable contributor.Irrational energy structures negatively influenced the high-quality development of the carbon-intensive sectors,and environmental regulation and foreign direct investment(FDI)have not yet significantly impacted GTFP.Based on these findings,this paper suggests that the carbon-intensive sectors should expedite their green transitions by focusing on system improvement,technological innovations,energy revolutions,and high-level opening up.展开更多
At present,it is impossible to deny the existence of artificial intelligence in various areas of social life,understood as the simulation of expert human intelligence from computer processes that involve learning,reas...At present,it is impossible to deny the existence of artificial intelligence in various areas of social life,understood as the simulation of expert human intelligence from computer processes that involve learning,reasoning,and self-correction,its benefits to the medical field,in particular,are innumerable,but their incorporation into health systems has been gradual for many reasons.According to the above,this research analyzed artificial intelligence based on resilient leadership in the health sector,for which qualitative research was carried out with a documentary-bibliographic design with printed and electronic documentary sources with theoretical contributions fromÁvila,Mayer,and Quesada[1],Morgan[2],Villa[3],and Finol[4],among others.It is highlighted that resilient leadership has become a strategic factor in all organizations,since times of uncertainty and changes lead institutions to properly manage the incorporation of technologies specifically AI,achieving in this way that the centers and professionals in the field of health assume the needs of the contexts and the innovations of the same.It is concluded that resilient leadership will allow artificial intelligence in the health sector to generate higher levels of learning and adaptability to the transformations that are necessary,whose resistance would make its application difficult and in the long run it will leave behind professionals who refuse to assume the contributions of these innovative techniques in medical practice.展开更多
基金supported in part by the National Natural Science Foundations of CHINA(Grant No.61771392,No.61771390,No.61871322 and No.61501373)Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China(Grant No.201955053002 and No.20185553035)。
文摘In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.
基金part of the project“Research on the Investment Game and Market Improvement of Social Capital in Supporting Poverty Reduction and Development in Ethnic Regions in Western China” (16XMZ094)funded by the National Social Science Fund of China。
文摘This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors.The GTFP in the carbon-intensive sectors experienced a decline between 2006 and 2011,followed by an upward trend beginning in 2012.Technological progress was the primary driver of GTFP growth,while business size was also a notable contributor.Irrational energy structures negatively influenced the high-quality development of the carbon-intensive sectors,and environmental regulation and foreign direct investment(FDI)have not yet significantly impacted GTFP.Based on these findings,this paper suggests that the carbon-intensive sectors should expedite their green transitions by focusing on system improvement,technological innovations,energy revolutions,and high-level opening up.
文摘At present,it is impossible to deny the existence of artificial intelligence in various areas of social life,understood as the simulation of expert human intelligence from computer processes that involve learning,reasoning,and self-correction,its benefits to the medical field,in particular,are innumerable,but their incorporation into health systems has been gradual for many reasons.According to the above,this research analyzed artificial intelligence based on resilient leadership in the health sector,for which qualitative research was carried out with a documentary-bibliographic design with printed and electronic documentary sources with theoretical contributions fromÁvila,Mayer,and Quesada[1],Morgan[2],Villa[3],and Finol[4],among others.It is highlighted that resilient leadership has become a strategic factor in all organizations,since times of uncertainty and changes lead institutions to properly manage the incorporation of technologies specifically AI,achieving in this way that the centers and professionals in the field of health assume the needs of the contexts and the innovations of the same.It is concluded that resilient leadership will allow artificial intelligence in the health sector to generate higher levels of learning and adaptability to the transformations that are necessary,whose resistance would make its application difficult and in the long run it will leave behind professionals who refuse to assume the contributions of these innovative techniques in medical practice.