The network availability differs in the Quality of Service (QoS) of different network services and constrains each other with performance expense. In order to solve the problems, Markov chain theory was introduced, a new Markov Chain and QoS based Network Availability (MCQNA) evaluation model was constructed on the basis of defining the matching degree of service capability based on the minimum service expense. Firstly, starting from the QoS indicator that could best reflect the characteristics of network availability and considering the performance overhead, the cost function was defined and the state transition matrix was given. Then, through the analysis of relationship between stationary state and network availability, the stationary distribution was solved and used as the dynamic weight of operation cost of QoS. The network availability evaluation characterized by the minimum operation cost of service was realized. The simulation results show that, the ergodic transition matrix constructed by the proposed model has the stationary distribution, and it is feasible to evaluate the network availability. According to the QoS standards of different services, the proposed model can be used to measure the network availability effectively for specific services.

In multi-hop routing protocol of Wireless Sensor Network (WSN), the cluster heads near the Sink node consumes faster than other cluster heads, the cluster heads are uneven distributed, and multi-hop links are not effective. To solved these problems, a low energy consumption multi-hop routing protocol based on control was proposed. The clusters' number and size, the multi-hop links' energy consumption, the number of rounds and the quantity of the data transmission were controlled. The simulation results show that this routing protocol prolongs the network's stable period time by 138% and 13%, and it also prolongs the network's lifetime by 13% and 8% in comparison with the Low Energy Adaptive Clustering Hierarch (LEACH) and uneven cluster-based routing protocol for wireless sensor networks (EEUC). Therefore, the proposed protocol can reduce the energy consumption of the network, balances network load, and prolongs the survival time of the network efficiently.

The problem of birth defects is one of the most important public health problems in the world, and the application of data mining method to improve the diagnostic accuracy for birth defects is a hot medical research issue. The authors proposed two emerging patterns for birth defects feature extraction: the defection contrast to normal and the normal contrast to defection. The Birth Defects Detection based on Emerging Patterns (BDD-EP) algorithm was implemented through combining the proposed patterns with C4.5 decision tree. The extensive experimental results show that the detection accuracy of BDD-EP is as high as 90.1%, the F-measure of normal samples is 93.9%, and the F-measure of defect samples is 741%. Compared with other famous classical classification algorithms, BDD-EP algorithm can get better results.

The model of service-oriented middleware based on message,could easily realize the load balancing of system after introducing the work principle of Web Services;using the logic communication routeway of MOM.It could achieve high flexibility and support the classic C/S service pattern.