The nervous system of the silkworm is vital for the development of organisms.It achieves and maintains normal life activities by regulating the function of the organs and all kinds of physiological processes in the si...The nervous system of the silkworm is vital for the development of organisms.It achieves and maintains normal life activities by regulating the function of the organs and all kinds of physiological processes in the silkworm.BmApontic(BmApt),as an imports nt bZIP tran scripti on factor,is required for the formatio n of pigme ntation in the silkworm.However,the fun ction of BmApt in the development of the nervous system of the silkworm remains unclear.Here,we showed that amino acid seque nee of BmApt was evoluti on arily con served in its Myb/SANT motif and basic DNA bindi ng domain.BmApt was expressed in the nervous system at the embry onic stage.Knockdow n of Bmapt by RNA interfere nee resulted in abno rmal development of axons.Moreover,the expression of BmnetrinA,BmnetrinB and Bmfrazzled was decreased in the Bmapt knockdown embryos.These results dem on strate that BmApt controls neurodevelopme nt by activati ng the expressi on of Bmnetrin and Bmfrazzled.展开更多
In this paper, we consider a BMAP/G/1 G-queue with setup times and multiple vacations. Arrivals of positive customers and negative customers follow a batch Markovian arrival process （BMAP） and Markovian arrival proc...In this paper, we consider a BMAP/G/1 G-queue with setup times and multiple vacations. Arrivals of positive customers and negative customers follow a batch Markovian arrival process （BMAP） and Markovian arrival process （MAP） respectively. The arrival of a negative customer removes all the customers in the system when the server is working. The server leaves for a vacation as soon as the system empties and is allowed to take repeated （multiple） vacations. By using the supplementary variables method and the censoring technique, we obtain the queue length distributions. We also obtain the mean of the busy period based on the renewal theory.展开更多
基于最小集覆盖理论的拥塞链路推理算法,仅对共享瓶颈链路进行推理,当拥塞路径存在多条链路拥塞时,算法的推理性能急剧下降.针对该问题,提出一种基于贝叶斯最大后验(Bayesian maximum a-posterior,简称BMAP)改进的拉格朗日松弛次梯度推...基于最小集覆盖理论的拥塞链路推理算法,仅对共享瓶颈链路进行推理,当拥塞路径存在多条链路拥塞时,算法的推理性能急剧下降.针对该问题,提出一种基于贝叶斯最大后验(Bayesian maximum a-posterior,简称BMAP)改进的拉格朗日松弛次梯度推理算法(Lagrange relaxation sub-gradient algorithm based on BMAP,简称LRSBMAP).针对推理算法中链路覆盖范围对算法推理性能的影响,以及探针部署及额外E2E路径探测发包的开销问题,提出设置度阈值(degree threshold value,简称DTV)参数预选待测IP网络收发包路由器节点,通过引入优选系数?,在保证链路覆盖范围的基础上,兼顾开销问题,确保算法的推理性能.针对大规模IP网络多链路拥塞场景下,链路先验概率求解方程组系数矩阵的稀疏性,提出一种对称逐次超松弛(symmetry successive over-relaxation,简称SSOR)分裂预处理共轭梯度法(preconditioned conjugate gradient method based on SSOR,简称PCG_SSOR)求解链路先验概率近似唯一解的方法,防止算法求解失败.实验验证了所提算法的准确性及鲁棒性.展开更多
A batch Markov arrival process（BMAP） X^＊=（N, J） is a 2-dimensional Markov process with two components, one is the counting process N and the other one is the phase process J. It is proved that the phase process i...A batch Markov arrival process（BMAP） X^＊=（N, J） is a 2-dimensional Markov process with two components, one is the counting process N and the other one is the phase process J. It is proved that the phase process is a time-homogeneous Markov chain with a finite state-space, or for short, Markov chain. In this paper,a new and inverse problem is proposed firstly： given a Markov chain J, can we deploy a process N such that the 2-dimensional process X^＊=（N, J） is a BMAP？ The process X^＊=（N, J） is said to be an adjoining BMAP for the Markov chain J. For a given Markov chain the adjoining processes exist and they are not unique. Two kinds of adjoining BMAPs have been constructed. One is the BMAPs with fixed constant batches, the other one is the BMAPs with independent and identically distributed（i.i.d） random batches. The method we used in this paper is not the usual matrix-analytic method of studying BMAP, it is a path-analytic method. We constructed directly sample paths of adjoining BMAPs. The expressions of characteristic（D_k, k = 0, 1, 2· · ·）and transition probabilities of the adjoining BMAP are obtained by the density matrix Q of the given Markov chain J. Moreover, we obtained two frontal Theorems. We present these expressions in the first time.展开更多
基金This work was supported by the National Natural Science Foundation of China(31571502,31872971 and 31602011),the Funds of"Shandong Double Tops*1 Program,China(SYL2017YSTD09)and the earmarked fund for China Agriculture Research System(CARS-22).
文摘The nervous system of the silkworm is vital for the development of organisms.It achieves and maintains normal life activities by regulating the function of the organs and all kinds of physiological processes in the silkworm.BmApontic(BmApt),as an imports nt bZIP tran scripti on factor,is required for the formatio n of pigme ntation in the silkworm.However,the fun ction of BmApt in the development of the nervous system of the silkworm remains unclear.Here,we showed that amino acid seque nee of BmApt was evoluti on arily con served in its Myb/SANT motif and basic DNA bindi ng domain.BmApt was expressed in the nervous system at the embry onic stage.Knockdow n of Bmapt by RNA interfere nee resulted in abno rmal development of axons.Moreover,the expression of BmnetrinA,BmnetrinB and Bmfrazzled was decreased in the Bmapt knockdown embryos.These results dem on strate that BmApt controls neurodevelopme nt by activati ng the expressi on of Bmnetrin and Bmfrazzled.
基金supported by the National Natural Science Foundation of China (No. 10871064)
文摘In this paper, we consider a BMAP/G/1 G-queue with setup times and multiple vacations. Arrivals of positive customers and negative customers follow a batch Markovian arrival process （BMAP） and Markovian arrival process （MAP） respectively. The arrival of a negative customer removes all the customers in the system when the server is working. The server leaves for a vacation as soon as the system empties and is allowed to take repeated （multiple） vacations. By using the supplementary variables method and the censoring technique, we obtain the queue length distributions. We also obtain the mean of the busy period based on the renewal theory.
文摘基于最小集覆盖理论的拥塞链路推理算法,仅对共享瓶颈链路进行推理,当拥塞路径存在多条链路拥塞时,算法的推理性能急剧下降.针对该问题,提出一种基于贝叶斯最大后验(Bayesian maximum a-posterior,简称BMAP)改进的拉格朗日松弛次梯度推理算法(Lagrange relaxation sub-gradient algorithm based on BMAP,简称LRSBMAP).针对推理算法中链路覆盖范围对算法推理性能的影响,以及探针部署及额外E2E路径探测发包的开销问题,提出设置度阈值(degree threshold value,简称DTV)参数预选待测IP网络收发包路由器节点,通过引入优选系数?,在保证链路覆盖范围的基础上,兼顾开销问题,确保算法的推理性能.针对大规模IP网络多链路拥塞场景下,链路先验概率求解方程组系数矩阵的稀疏性,提出一种对称逐次超松弛(symmetry successive over-relaxation,简称SSOR)分裂预处理共轭梯度法(preconditioned conjugate gradient method based on SSOR,简称PCG_SSOR)求解链路先验概率近似唯一解的方法,防止算法求解失败.实验验证了所提算法的准确性及鲁棒性.
基金Supported by the National Natural Science Foundation of China(No.11671132,11601147)Hunan Provincial Natural Science Foundation of China(No.16J3010)+1 种基金Philosophy and Social Science Foundation of Hunan Province(No.16YBA053)Key Scientific Research Project of Hunan Provincial Education Department(No.15A032)
文摘A batch Markov arrival process（BMAP） X^＊=（N, J） is a 2-dimensional Markov process with two components, one is the counting process N and the other one is the phase process J. It is proved that the phase process is a time-homogeneous Markov chain with a finite state-space, or for short, Markov chain. In this paper,a new and inverse problem is proposed firstly： given a Markov chain J, can we deploy a process N such that the 2-dimensional process X^＊=（N, J） is a BMAP？ The process X^＊=（N, J） is said to be an adjoining BMAP for the Markov chain J. For a given Markov chain the adjoining processes exist and they are not unique. Two kinds of adjoining BMAPs have been constructed. One is the BMAPs with fixed constant batches, the other one is the BMAPs with independent and identically distributed（i.i.d） random batches. The method we used in this paper is not the usual matrix-analytic method of studying BMAP, it is a path-analytic method. We constructed directly sample paths of adjoining BMAPs. The expressions of characteristic（D_k, k = 0, 1, 2· · ·）and transition probabilities of the adjoining BMAP are obtained by the density matrix Q of the given Markov chain J. Moreover, we obtained two frontal Theorems. We present these expressions in the first time.
