Delay Trigger in the Application of Network Testing System

The researchers who study the local area network( LAN) eXtension for instrumentation( LXI) instrument are pursuing instrument's high-precision synchronization. In the paper,three synchronization modes were discussed which were clock synchronization, trigger synchronization, and response synchronization. Synchronous process between LXI instruments was analyzed and each time factor affecting the synchronization accuracy was discussed. On the basis of the analysis,it can be found that delay trigger plays an important role in the network testing system's synchronization. Delay trigger can produce an additional time interval to correct the difference of each LXI instrument's response time. Then,a method to realize the delay trigger was introduced. Delay time can be adjustable according to the actual demand. Finally,synchronization accuracy of network testing system can reach nanoseconds.

New artificial neural networks for true triaxial stress state analysis and demonstration of intermediate principal stress effects on intact rock strength

Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.

Towards Fast and Efficient Algorithm for Learning Bayesian Network

Learning Bayesian network structure is one of the most exciting challenges in machine learning. Discovering a correct skeleton of a directed acyclic graph（DAG） is the foundation for dependency analysis algorithms for this problem. Considering the unreliability of high order condition independence（CI） tests, and to improve the efficiency of a dependency analysis algorithm, the key steps are to use few numbers of CI tests and reduce the sizes of conditioning sets as much as possible. Based on these reasons and inspired by the algorithm PC, we present an algorithm, named fast and efficient PC（FEPC）, for learning the adjacent neighbourhood of every variable. FEPC implements the CI tests by three kinds of orders, which reduces the high order CI tests significantly. Compared with current algorithm proposals, the experiment results show that FEPC has better accuracy with fewer numbers of condition independence tests and smaller size of conditioning sets. The highest reduction percentage of CI test is 83.3% by EFPC compared with PC algorithm.

艾灸对轻度认知障碍患者认知注意网络功能的影响

Objective:To explore the effect differences between moxibustion and donepezil hydrochloride on the attention network function of patients with mild cognitive impairment(MCI).Methods:A total of 64 patients of MCI were randomly divided into the moxibustion group and donepezil hydrochloride group,32 cases in each one.On the basis of conventional treatment,the patients in the moxibustion group were given moxibustion,6 times a week,and the patients in the donepezil hydrochloride group were given donepezil hydrochloride orally,5 mg/day.The course of treatment was 60 days for both of the groups.Cognitive attention network function and activities of daily living(ADL)score were examined before and after treatment.Results:The differences of alerting reaction time(RT),executive control RT,overall mean RT and accuracy of the moxibustion group after treatment were significantly higher than those of the donepezil hydrochloride group[alert:(60.3±3.3)ms vs(48.3±3.7)ms,P<0.05;executive control:(81.2±3.2)ms vs(91.7±4.2)ms,P<0.05;total reaction time:(500.4±17.2)ms vs(536.2±20.1)ms.P<0.05;accuracy:(83.7±4.6)%vs(77.4±4.3)%,P<0.05].After treatment,the ADL scores of the both groups were significantly higher than those before treatment[the moxibustion group:(56.47±4.02)points vs(41.53±4.06)points,P<0.05;the donepezil hydrochloride group:(50.75±4.05)points vs(40.84±3.67)points,P<0.05],and the ADL score of the moxibustion group was significantly higher than that of the donepezil hydrochloride group[(56.47±4.02)points vs(50.75±4.05)points,P<0.05].Conclusion:Compared with donepezil hydrochloride,moxibustion has a better effect on the cognitive function of MCI patients.