Percolation transitions in edge-coupled interdependent networks with directed dependency links

We propose a model of edge-coupled interdependent networks with directed dependency links(EINDDLs)and develop the theoretical analysis framework of this model based on the self-consistent probabilities method.The phase transition behaviors and parameter thresholds of this model under random attacks are analyzed theoretically on both random regular(RR)networks and Erd¨os-Renyi(ER)networks,and computer simulations are performed to verify the results.In this EINDDL model,a fractionβof connectivity links within network B depends on network A and a fraction(1-β)of connectivity links within network A depends on network B.It is found that randomly removing a fraction(1-p)of connectivity links in network A at the initial state,network A exhibits different types of phase transitions(first order,second order and hybrid).Network B is rarely affected by cascading failure whenβis small,and network B will gradually converge from the first-order to the second-order phase transition asβincreases.We present the critical values ofβfor the phase change process of networks A and B,and give the critical values of p andβfor network B at the critical point of collapse.Furthermore,a cascading prevention strategy is proposed.The findings are of great significance for understanding the robustness of EINDDLs.

The dependences of osteocyte network on bone compartment, age, and disease

Osteocytes, the most abundant bone cells, form an interconnected network in the lacunar-canalicular pore system （LCS） buried within the mineralized matrix, which allows osteocytes to obtain nutrients from the blood supply, sense external mechanical signals, and communicate among themselves and with other cells on bone surfaces. In this study, we examined key features of the LCS network including the topological parameter and the detailed structure of individual connections and their variations in cortical and cancellous compa~ tments, at different ages, and in two disease conditions with altered mechanosensing （perlecan deficiency and diabetes）. LCS network showed both topological stability, in terms of conservation of connectivity among osteocyte lacunae （similar to the ＂nodes＂ in a computer network）, and considerable variability the pericellular annular fluid gap surrounding lacunae and canaliculi （similar to the ＂bandwidth＂ of individual links in a computer network）. Age, in the range of our study （15-32 weeks）, affected only the pericellular fluid annulus in cortical bone but not in cancellous bone. Diabetes impacted the spacing of the lacunae, while the perlecan deficiency had a profound influence on the pericellular fluid annulus. The LCS network features play important roles in osteocyte signaling and regulation of bone growth and adaptation.

Simulation of Service Supply Chainly Formation Based on Mobile Agent

In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respond the sub-requests cooperatively. Thus the Service Supply Chain (SSC) can be formed. But the existing bottom-up and up-bottom supply chain formation fashions cannot be adapted to the SSC in distributed environment of E-Commerce. Task Dependency Network is exploited to illustrate the service relationship among consumers and suppliers. The formation of SSC with some simulations is elaborated. Then the influence on the formation of SSC caused by the type of service suppliers, the quantities of MA and its variety in number is elucidated.

Degree dependence entropy descriptor for complex networks

In order to supply better accordance for mod eling and simulation of complex networks, a new degree dependence entropy （DDE） descriptor is proposed to describe the degree dependence relationship and corre sponding characteristic in this paper. First of all, degrees of vertices and the shortest path lengths between all pairs of ,ertices are computed. Then the degree dependence matrices under different shortest path lengths are con structed. At last the DDEs are extracted from the degree dependence matrices. Simulation results show that the DDE descriptor can reflect the complexity of degree dependence relationship in complex networks; high DDE indicates complex degree dependence relationship; low DDE indicates the opposite one. The DDE can be seen as a quantitative statistical characteristic, which is meaningful for networked modeling and simulation.