搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

动作电位动态变化对螺旋波演化行为的影响

关富荣 李成乾 邓敏艺

引用本文:
Citation:

动作电位动态变化对螺旋波演化行为的影响

关富荣, 李成乾, 邓敏艺

Effects of dynamic change of action potential on evolution behavior of spiral wave

Guan Fu-Rong, Li Cheng-Qian, Deng Min-Yi
PDF
HTML
导出引用
  • 在心脏病患者的心脏中观察到心室不同部位的心肌细胞动作电位时长(APD)恢复曲线陡峭程度差别很大, 而陡峭的APD恢复曲线既可以在某些情况下导致螺旋波破碎和心室纤维性颤动, 也可能在另一些情况下不导致螺旋波破碎, 螺旋波动力学行为与陡峭的APD恢复曲线的关系仍未完全清楚, 因此需要深入研究. 本文采用二维可激发介质元胞自动机模型, 研究陡峭程度不同的APD恢复曲线下螺旋波的动力学行为, 数值模拟结果表明: 陡峭的APD恢复曲线可以使漫游螺旋波稳定, 也可以促进螺旋波漫游或引起破碎, 甚至使螺旋波消失, 观察到在APD恢复曲线总平均斜率大于1情况下螺旋波仍维持稳定或漫游, 在APD恢复曲线总平均斜率比1小很多的情况下螺旋波出现破碎; 在APD恢复曲线总平均斜率大于1的情况下观察到多普勒失稳、爱克豪斯失稳和APD交替变化三种螺旋波破碎方式, 其中APD交替变化导致的螺旋波破碎可在APD恢复曲线总平均斜率比1小很多的情况下发生. 观察到螺旋波通过漫游出系统边界和遇到传导障碍消失. 此外, 还发现通过增大元胞APD有利于防止螺旋波破碎. 对产生这些现象的物理机制进行解释.
    It is observed in cardiac patients that the steepnesses of action potential duration (APD) restitution curve of cardiomyocytes in different regions of the ventricle are significantly different from region to region. However, the steep APD restitution curve can either lead the spiral wave to break up and set up the ventricular fibrillation in certain conditions or result in no breakup of spiral wave in other conditions. The relationship between the dynamic behavior of spiral wave and steep APD restitution curve is still not completely clear. Therefore, further research is needed. In this paper, a two-dimensional excitable medium cellular automata model is used to study the influences of the APD restitution curves with different steepnesses on the dynamic behavior of spiral wave. Numerical simulation results show that the steep APD restitution curve can stabilize the meandering spiral wave, causing the stable spiral wave to roam or break, and even to disappear. When the total average slope of APD restitution curve is greater than 1, it is observed that spiral wave may be still stable or meandering. When the total average slope of APD restitution curve is much smaller than 1, the breakup of spiral waves may occur. Three types of spiral wave breakups are observed. They are the Doppler instability, Eckhaus instability, and APD alternation. The Doppler instability and Eckhaus instability are related to the total average slope of APD restitution curve greater than 1, and the spiral wave breakup caused by APD alternans may occur when the total average slope of APD restitution curve is much smaller than 1. When the total average slope of APD restitution curve is greater than 1, the phenomena that spiral waves disappear by meandering out of the system boundary and conduction barriers are observed. In addition, we also find that increasing cellular APD is beneficial to preventing spiral wave from breaking up. The physical mechanisms behind those phenomena are heuristically analyzed.
      通信作者: 邓敏艺, dengminyi@mailbox.gxnu.edu.cn
    • 基金项目: 国家自然科学基金 (批准号: 12047567)资助的课题
      Corresponding author: Deng Min-Yi, dengminyi@mailbox.gxnu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 12047567).
    [1]

    Cross M C, Hohenberg P C 1993 Rev. Mod. Phys. 65 851Google Scholar

    [2]

    Frisch T, Rica S, Coullet P, Gilli J M 1994 Phys. Rev. Lett. 72 1471Google Scholar

    [3]

    Winfree A T 1972 Science 175 634Google Scholar

    [4]

    Gorelova N A, Bures J 1983 J. Neurobiol. 14 353Google Scholar

    [5]

    Huang X, Xu W, Liang J, Takagaki K, Gao X, Wu J Y 2010 Neuron 68 978Google Scholar

    [6]

    Pandit S V, Jalife J 2013 Circ. Res. 112 849Google Scholar

    [7]

    Narayan S M, Krummen D E, Rappel W J 2012 J. Cardiovasc. Electr. 23 447Google Scholar

    [8]

    Lip G Y H, Fauchier L, Freedman S B, Gelder I V, Natale A, Gianni C, Nattel S, Potpara T, Rienstra M, Tse H F, Lane D A 2016 Nat. Rev. Dis. Primers 2 16016Google Scholar

    [9]

    Gray R A, Jalife J, Panfilov A V, Baxter W T, Cabo C, Davidenko J M, Pertsov A M 1995 Science 270 1222Google Scholar

    [10]

    Christoph J, Chebbok M, Richter C, Schetelig J S, Bittihn P, Stein S, Uzelac I, Fenton F H 2018 Nature 555 667Google Scholar

    [11]

    Davidenko J M, Pertsov A V, Salomonsz R, Baxter W, Jalife J 1992 Nature 355 349Google Scholar

    [12]

    Kinoshita S, Iwamoto M, Tateishi K, Suematsu N J, Ueyama D 2013 Phys. Rev. E 87 062815Google Scholar

    [13]

    Luther S, Fenton F H, Kornreich B G, Squires A, Bittihn P, Hornung D 2011 Nature 475 235Google Scholar

    [14]

    潘军廷, 何银杰, 夏远勋, 张宏 2020 69 080503Google Scholar

    Pan J T, He Y J, Xia Y X, Zhang H 2020 Acta Phys. Sin. 69 080503Google Scholar

    [15]

    Fenton F H, Cherry E M, Hastings H M, Evans S J 2002 Chaos 12 852Google Scholar

    [16]

    Ng G A 2017 Pharmacol. Therapeut. 176 1Google Scholar

    [17]

    Rosenbaum D S, Kaplan D T, Kanai A, Jackson L, Garan H, Cohen R J, Salama G 1991 Circulation 84 1333Google Scholar

    [18]

    Banville I, Gray R A 2002 J. Cardiovasc. Elect. 13 1141Google Scholar

    [19]

    Berger R D 2004 Circ. Res. 94 567Google Scholar

    [20]

    Qu Z, Weiss J N 2015 Annu. Rev. Physiol. 77 29Google Scholar

    [21]

    Vandersickel N, Defauw A, Dawyndt P, Panflov A V 2016 Sci. Rep. 6 29397Google Scholar

    [22]

    Avula U M R, Abrams J, Katchman A, Zakharov S, Mironov S, Bayne J, Roybal D, Gorti A 2019 Jci Insight 5 e128765Google Scholar

    [23]

    Handa B S, Lawal S, Wright I J, Li X, García J C, Mansfield C, Chowdhury R A, Peters N S 2019 Front. Cardiovasc. Med. 6 34Google Scholar

    [24]

    Zheng Y, Wei D, Zhu X, Chen W, Fukuda K, Shimokawa H 2015 Comput. Biol. Med. 63 261Google Scholar

    [25]

    Garfinkel A, Kim Y H, Voroshilovsky O, Qu Z, Kil J R, Lee M H, Karagueuzian H S, Weiss J N, Chen P S 2000 Proc. Natl. Acad. Sci. U. S. A. 97 6061Google Scholar

    [26]

    Zhang N, Luo Q, Jin Q, Han Y, Huang S, Wei Y, Lin C, Chen K, Shen W, Wu L 2020 Cardiovasc. Drugs Ther. 34 323Google Scholar

    [27]

    Qu Z, Garfinkel A, Chen P S, Weiss J N 2000 Circulation 102 1664Google Scholar

    [28]

    Franz M R, Jamal S M, Narayan S M 2012 Europace 14 v58Google Scholar

    [29]

    Alonso S, Bär M, Echebarria B 2016 Rep. Prog. Phys. 79 096601Google Scholar

    [30]

    Clayton R H, Taggart P 2005 Biomed. Eng. Online 4 54Google Scholar

    [31]

    Nash M P, Bradley C P, Sutton P M, Clayton R H, Kallis P, Hayward M P, Paterson D J, Taggart P 2006 Exp. Physiol. 91 339Google Scholar

    [32]

    Bub G, Shrier A, Glass L 2005 Phys. Rev. Lett. 94 028105Google Scholar

    [33]

    Deng M Y, Dai J Y, Zhang X L 2015 Chin. Phys. B 24 090503Google Scholar

    [34]

    张学良, 谭惠丽, 唐国宁, 邓敏艺 2017 66 200501Google Scholar

    Zhang X L, Tan H L, Tang G N, Deng M Y 2017 Acta Phys. Sin. 66 200501Google Scholar

    [35]

    Lin Y T, Chang E T Y, Eatock J, Galla T, Clayton R H 2017 J. R. Soc. Interface 14 20160968Google Scholar

    [36]

    Pak H N, Hong S J, Hwang G S, Lee H S, Park S W 2004 J. Cardiovasc. Electr. 15 1357Google Scholar

    [37]

    欧阳颀 2001 物理 30 30

    Ouyang Q 2001 Physics 30 30

  • 图 1  $ \alpha = 40 $和不同$ \beta , \gamma $下APD恢复曲线(a)和斜率$ \lambda $随舒张间隔DI的变化(b)

    Fig. 1.  APD restitution curve (a) and the change of its slope $ \lambda $ with diastolic interval (DI) (b) for $ \alpha = 40 $ and different values of $ \beta $ and $ \gamma $

    图 2  在参数$ \alpha = 40 $, $ \gamma = 10 $, $ \beta = 14 $下不同时刻的膜电位斑图 (a)$ t = 0 $; (b)$ t = 20000 $; (c)$ t = 60000 $; (d)$ t = 100000 $

    Fig. 2.  Patterns of the membrane potential at different time moments for $ \alpha = 40 $, $ \gamma = 10 $ and $ \beta = 14 $: (a)$ t = 0 $; (b)$ t = 20000 $; (c)$ t = 60000 $; (d)$ t = 100000 $.

    图 3  在参数$ \alpha = 40 $, $ \gamma = 10 $, $ \beta = 15 $下不同时刻的膜电位斑图, 白色代表静息态; 黑色代表激发态; 绿色(红色)代表元胞APD在[26, 30] ([31, 40])范围的不应态 (a) $ t = 0 $; (b) $ t = 400 $; (c) $ t = 490 $; (d) $ t = 510 $; (e) $ t = 560 $; (f) $ t = 1800 $; (g) $ t = 2000 $; (h) $ t = 3070 $

    Fig. 3.  Patterns of the membrane potential at different time moments for $ \alpha = 40 $, $ \gamma = 10 $ and $ \beta = 15 $. The white and black represent the rest-state and excited state, respectively, the green (red) represents the refractory states in which the APD of the cell is in the range of [26,30] ([31, 40]): (a)$ t = 0 $; (b)$ t = 400 $; (c)$ t = 490 $; (d)$ t = 510 $; (e)$ t = 560 $; (f)$ t = 1800 $; (g)$ t = 2000 $; (h)$ t = 3070 $.

    图 4  在参数$ \alpha = 40 $, $ \gamma = 10 $, $ \beta = 18 $下不同时刻的膜电位斑图, 作图方式与图3一致 (a)$ t = 0 $; (b)$ t = 40 $; (c)$ t = 220 $; (d)$ t = 250 $; (e)$ t = 255 $; (f)$ t = 310 $; (g)$ t = 2850 $; (h)$ t = 34300 $

    Fig. 4.  Patterns of the membrane potential at different time moments for $ \alpha = 40 $, $ \gamma = 10 $ and $ \beta = 18 $, the drawing method is consistent with Fig. 3: (a)$ t = 0 $; (b)$ t = 40 $; (c)$ t = 220 $; (d)$ t = 250 $; (e)$ t = 255 $; (f)$ t = 310 $; (g)$ t = 2850 $; (h)$ t = 34300 $.

    图 5  在参数$ \alpha = 40 $, $ \gamma = 10 $, $ \beta = 33 $下不同时刻的膜电位斑图, 作图方式与图3一致 (a)$ t = 0 $; (b)$ t = 50 $; (c)$ t = 60 $; (d)$t = $$ 100$; (e)$ t = 130 $; (f)$ t = 150 $; (g)$ t = 200 $; (h)$ t = 500 $

    Fig. 5.  Patterns of the membrane potential at different time moments for $ \alpha = 40 $, $ \gamma = 10 $ and $ \beta = 33 $, the drawing method is consistent with Fig. 3: (a)$ t = 0 $; (b)$ t = 50 $; (c)$ t = 60 $; (d)$ t = 100 $; (e)$ t = 130 $; (f)$ t = 150 $; (g)$ t = 200 $; (h)$ t = 500 $.

    图 6  $\beta \text- \gamma$参数平面上的相图

    Fig. 6.  Phase diagram on $\beta \text- \gamma$ parameter plane.

    图 7  APD恢复曲线总平均斜率$ {\bar \lambda _{{\text{ T}}}} $随参数$ \beta , \gamma $变化的直方图

    Fig. 7.  Histogram of total average slope of APD restitution curve varying with parameters $ \beta $ and $ \gamma $.

    图 8  不同参数(螺旋波态)下恢复曲线的平均斜率随时间变化 (a)$ \beta = 3 $, $ \gamma = 1 $(稳定); (b)$ \beta = 4 $, $ \gamma = 1 $(稳定); (c)$ \beta = 7 $, $ \gamma = 17 $(稳定); (d)$ \beta = 4 $, $ \gamma = 2 $(漫游); (e)$ \beta = 5 $, $ \gamma = 1 $(漫游); (f)$ \beta = 10 $, $ \gamma = 9 $(漫游); (g)$ \beta = 13 $, $ \gamma = 5 $(第一种破碎); (h)$ \beta = 18 $, $ \gamma = 11 $(第二种破碎); (i)$ \beta = 38 $, $ \gamma = 15 $(第三种破碎); (j)$ \beta = 19 $, $ \gamma = 19 $(总平均斜率小于1的破碎); (k)$ \beta = 31 $, $ \gamma = 5 $(消失); (l)$ \beta = 32 $, $ \gamma = 8 $(消失)

    Fig. 8.  Evolution of the average slope of the restitution curve for different parameters (spiral wave states): (a)$ \beta = 3 $, $ \gamma = 1 $(stable); (b)$ \beta = 4 $, $ \gamma = 1 $(stable); (c)$ \beta = 7 $, $ \gamma = 17 $(stable); (d)$ \beta = 4 $, $ \gamma = 2 $(meandering); (e)$ \beta = 5 $, $ \gamma = 1 $(meandering); (f)$ \beta = 10 $, $ \gamma = 9 $(meandering); (g)$ \beta = 13 $, $ \gamma = 5 $(first type of breakup); (h)$ \beta = 18 $, $ \gamma = 11 $(second type of breakup); (i)$ \beta = 38 $, $ \gamma = 15 $(third type of breakup); (j)$ \beta = 19 $, $ \gamma = 19 $(breakup with total average slope less than 1); (k)$ \beta = 31 $, $ \gamma = 5 $(disappear); (l)$ \beta = 32 $, $ \gamma = 8 $(disappear).

    图 9  在参数$ \alpha = 40 $, $ \gamma = 19 $, $ \beta = 19 $下不同时刻的膜电位斑图, 作图方式与图3相同 (a)$ t = 0 $; (b)$ t = 27 $; (c)$ t = 37 $; (d)$ t = 40 $; (e)$ t = 57 $; (f)$ t = 80 $; (g)$ t = 90 $; (h)$ t = 113 $; (i)$ t = 1900 $

    Fig. 9.  Patterns of the membrane potential at different time moments for $ \alpha = 40 $, $ \gamma = 19 $ and $ \beta = 19 $. The drawing method is consistent with Fig. 3: (a)$ t = 0 $; (b)$ t = 27 $; (c)$ t = 37 $; (d)$ t = 40 $; (e)$ t = 57 $; (f)$ t = 80 $; (g)$ t = 90 $; (h)$ t = 113 $; (i)$ t = 1900 $.

    图 10  在不同$ {n_0} $情况下$\beta \text- \alpha$参数平面上的相图 (a)$ {n_0} = 25 $; (b)$ {n_0} = 40 $; (c)$ {n_0} = 55 $.

    Fig. 10.  Phase diagram on $\beta \text- \alpha$ parameter plane for different values of $ {n_0} $: (a)$ {n_0} = 25 $; (b)$ {n_0} = 40 $; (c)$ {n_0} = 55 $.

    Baidu
  • [1]

    Cross M C, Hohenberg P C 1993 Rev. Mod. Phys. 65 851Google Scholar

    [2]

    Frisch T, Rica S, Coullet P, Gilli J M 1994 Phys. Rev. Lett. 72 1471Google Scholar

    [3]

    Winfree A T 1972 Science 175 634Google Scholar

    [4]

    Gorelova N A, Bures J 1983 J. Neurobiol. 14 353Google Scholar

    [5]

    Huang X, Xu W, Liang J, Takagaki K, Gao X, Wu J Y 2010 Neuron 68 978Google Scholar

    [6]

    Pandit S V, Jalife J 2013 Circ. Res. 112 849Google Scholar

    [7]

    Narayan S M, Krummen D E, Rappel W J 2012 J. Cardiovasc. Electr. 23 447Google Scholar

    [8]

    Lip G Y H, Fauchier L, Freedman S B, Gelder I V, Natale A, Gianni C, Nattel S, Potpara T, Rienstra M, Tse H F, Lane D A 2016 Nat. Rev. Dis. Primers 2 16016Google Scholar

    [9]

    Gray R A, Jalife J, Panfilov A V, Baxter W T, Cabo C, Davidenko J M, Pertsov A M 1995 Science 270 1222Google Scholar

    [10]

    Christoph J, Chebbok M, Richter C, Schetelig J S, Bittihn P, Stein S, Uzelac I, Fenton F H 2018 Nature 555 667Google Scholar

    [11]

    Davidenko J M, Pertsov A V, Salomonsz R, Baxter W, Jalife J 1992 Nature 355 349Google Scholar

    [12]

    Kinoshita S, Iwamoto M, Tateishi K, Suematsu N J, Ueyama D 2013 Phys. Rev. E 87 062815Google Scholar

    [13]

    Luther S, Fenton F H, Kornreich B G, Squires A, Bittihn P, Hornung D 2011 Nature 475 235Google Scholar

    [14]

    潘军廷, 何银杰, 夏远勋, 张宏 2020 69 080503Google Scholar

    Pan J T, He Y J, Xia Y X, Zhang H 2020 Acta Phys. Sin. 69 080503Google Scholar

    [15]

    Fenton F H, Cherry E M, Hastings H M, Evans S J 2002 Chaos 12 852Google Scholar

    [16]

    Ng G A 2017 Pharmacol. Therapeut. 176 1Google Scholar

    [17]

    Rosenbaum D S, Kaplan D T, Kanai A, Jackson L, Garan H, Cohen R J, Salama G 1991 Circulation 84 1333Google Scholar

    [18]

    Banville I, Gray R A 2002 J. Cardiovasc. Elect. 13 1141Google Scholar

    [19]

    Berger R D 2004 Circ. Res. 94 567Google Scholar

    [20]

    Qu Z, Weiss J N 2015 Annu. Rev. Physiol. 77 29Google Scholar

    [21]

    Vandersickel N, Defauw A, Dawyndt P, Panflov A V 2016 Sci. Rep. 6 29397Google Scholar

    [22]

    Avula U M R, Abrams J, Katchman A, Zakharov S, Mironov S, Bayne J, Roybal D, Gorti A 2019 Jci Insight 5 e128765Google Scholar

    [23]

    Handa B S, Lawal S, Wright I J, Li X, García J C, Mansfield C, Chowdhury R A, Peters N S 2019 Front. Cardiovasc. Med. 6 34Google Scholar

    [24]

    Zheng Y, Wei D, Zhu X, Chen W, Fukuda K, Shimokawa H 2015 Comput. Biol. Med. 63 261Google Scholar

    [25]

    Garfinkel A, Kim Y H, Voroshilovsky O, Qu Z, Kil J R, Lee M H, Karagueuzian H S, Weiss J N, Chen P S 2000 Proc. Natl. Acad. Sci. U. S. A. 97 6061Google Scholar

    [26]

    Zhang N, Luo Q, Jin Q, Han Y, Huang S, Wei Y, Lin C, Chen K, Shen W, Wu L 2020 Cardiovasc. Drugs Ther. 34 323Google Scholar

    [27]

    Qu Z, Garfinkel A, Chen P S, Weiss J N 2000 Circulation 102 1664Google Scholar

    [28]

    Franz M R, Jamal S M, Narayan S M 2012 Europace 14 v58Google Scholar

    [29]

    Alonso S, Bär M, Echebarria B 2016 Rep. Prog. Phys. 79 096601Google Scholar

    [30]

    Clayton R H, Taggart P 2005 Biomed. Eng. Online 4 54Google Scholar

    [31]

    Nash M P, Bradley C P, Sutton P M, Clayton R H, Kallis P, Hayward M P, Paterson D J, Taggart P 2006 Exp. Physiol. 91 339Google Scholar

    [32]

    Bub G, Shrier A, Glass L 2005 Phys. Rev. Lett. 94 028105Google Scholar

    [33]

    Deng M Y, Dai J Y, Zhang X L 2015 Chin. Phys. B 24 090503Google Scholar

    [34]

    张学良, 谭惠丽, 唐国宁, 邓敏艺 2017 66 200501Google Scholar

    Zhang X L, Tan H L, Tang G N, Deng M Y 2017 Acta Phys. Sin. 66 200501Google Scholar

    [35]

    Lin Y T, Chang E T Y, Eatock J, Galla T, Clayton R H 2017 J. R. Soc. Interface 14 20160968Google Scholar

    [36]

    Pak H N, Hong S J, Hwang G S, Lee H S, Park S W 2004 J. Cardiovasc. Electr. 15 1357Google Scholar

    [37]

    欧阳颀 2001 物理 30 30

    Ouyang Q 2001 Physics 30 30

  • [1] 汪芃, 李倩昀, 黄志精, 唐国宁. 在兴奋-抑制混沌神经元网络中有序波的自发形成.  , 2018, 67(17): 170501. doi: 10.7498/aps.67.20180506
    [2] 汪芃, 李倩昀, 唐国宁. Hindmarsh-Rose神经元阵列自发产生螺旋波的研究.  , 2018, 67(3): 030502. doi: 10.7498/aps.67.20172140
    [3] 梁经韵, 张莉莉, 栾悉道, 郭金林, 老松杨, 谢毓湘. 多路段元胞自动机交通流模型.  , 2017, 66(19): 194501. doi: 10.7498/aps.66.194501
    [4] 徐莹, 王春妮, 靳伍银, 马军. 梯度耦合下神经元网络中靶波和螺旋波的诱发研究.  , 2015, 64(19): 198701. doi: 10.7498/aps.64.198701
    [5] 乔成功, 李伟恒, 唐国宁. 细胞外钾离子浓度延迟恢复对螺旋波的影响研究.  , 2014, 63(23): 238201. doi: 10.7498/aps.63.238201
    [6] 永贵, 黄海军, 许岩. 菱形网格的行人疏散元胞自动机模型.  , 2013, 62(1): 010506. doi: 10.7498/aps.62.010506
    [7] 赵龙, 杨继平, 郑艳红. 神经元网络螺旋波诱发机理研究.  , 2013, 62(2): 028701. doi: 10.7498/aps.62.028701
    [8] 马军, 谢振博, 陈江星. 热敏神经元网络中螺旋波死亡和破裂的数值模拟.  , 2012, 61(3): 038701. doi: 10.7498/aps.61.038701
    [9] 田昌海, 邓敏艺, 孔令江, 刘慕仁. 螺旋波动力学性质的元胞自动机有向小世界网络研究.  , 2011, 60(8): 080505. doi: 10.7498/aps.60.080505
    [10] 李庆定, 董力耘, 戴世强. 公交车停靠诱发交通瓶颈的元胞自动机模拟.  , 2009, 58(11): 7584-7590. doi: 10.7498/aps.58.7584
    [11] 单博炜, 林鑫, 魏雷, 黄卫东. 纯物质枝晶凝固的元胞自动机模型.  , 2009, 58(2): 1132-1138. doi: 10.7498/aps.58.1132
    [12] 戴瑜, 唐国宁. 离散可激发介质激发性降低的几种起因.  , 2009, 58(3): 1491-1496. doi: 10.7498/aps.58.1491
    [13] 张立升, 邓敏艺, 孔令江, 刘慕仁, 唐国宁. 用元胞自动机模型研究二维激发介质中的非线性波.  , 2009, 58(7): 4493-4499. doi: 10.7498/aps.58.4493
    [14] 岳 昊, 邵春福, 陈晓明, 郝合瑞. 基于元胞自动机的对向行人交通流仿真研究.  , 2008, 57(11): 6901-6908. doi: 10.7498/aps.57.6901
    [15] 梅超群, 黄海军, 唐铁桥. 高速公路入匝控制的一个元胞自动机模型.  , 2008, 57(8): 4786-4793. doi: 10.7498/aps.57.4786
    [16] 张文铸, 袁 坚, 俞 哲, 徐赞新, 山秀明. 基于元胞自动机的无线传感网络整体行为研究.  , 2008, 57(11): 6896-6900. doi: 10.7498/aps.57.6896
    [17] 郭四玲, 韦艳芳, 薛 郁. 元胞自动机交通流模型的相变特性研究.  , 2006, 55(7): 3336-3342. doi: 10.7498/aps.55.3336
    [18] 吴可非, 孔令江, 刘慕仁. 双车道元胞自动机NS和WWH交通流混合模型的研究.  , 2006, 55(12): 6275-6280. doi: 10.7498/aps.55.6275
    [19] 花 伟, 林柏梁. 考虑行车状态的一维元胞自动机交通流模型.  , 2005, 54(6): 2595-2599. doi: 10.7498/aps.54.2595
    [20] 牟勇飚, 钟诚文. 基于安全驾驶的元胞自动机交通流模型.  , 2005, 54(12): 5597-5601. doi: 10.7498/aps.54.5597
计量
  • 文章访问数:  3896
  • PDF下载量:  58
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-01-05
  • 修回日期:  2022-02-25
  • 上网日期:  2022-05-29
  • 刊出日期:  2022-06-05

/

返回文章
返回
Baidu
map