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10.1073/pnas.1612094113 http://scihub22266oqcxt.onion/10.1073/pnas.1612094113 C5081585!5081585 !27698137     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27698137 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Natl+Acad+Sci+U+S+A 2016 ; 113 (42 ): 11726-11731 Nephropedia Template TP {{tp|p=27698137 |t=2016. System crash as dynamics of complex networks |pdf=|usr=}}{{27698137 }} gab.com Text System crash as dynamics of complex networks JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=27698137 #FOAvip Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the "peculiar" dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed. Twit Text FOAVip System crash as dynamics of complex networks ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=27698137 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27698137 #FOAvip English Wikipedia <ref>{{Cite pmid|27698137 }}</ref> System crash as dynamics of complex networks #MMPMID27698137 Yu Y ; Xiao G ; Zhou J ; Wang Y ; Wang Z ; Kurths J ; Schellnhuber HJ Proc Natl Acad Sci U S A 2016[Oct]; 113 (42 ): 11726-11731 PMID27698137 show ga Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the "peculiar" dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed. äSystem crash as dynamics of complex networks (epmc).pdf DeepDyve Pubget Overpricing