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.

Research on Testing Technologies of Network System in Smart Substations

The network structures of smart substations and the characteristics of industrial Ethernet switches are analyzed.The testing technologies of network systems based on smart substations are specifically elaborated.A viewpoint is proposed that special testing policy&method of smart substation networks should be followed,so that the results can reveal the real network data exchange performance of the whole station.This view ensures the safety and stability of smart substations and lays a foundation for future upgrades and expansions.

High-Speed EMU TCMS Design and LCC Technology Research

This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.

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.

The effect of moxibustion on cognitive attention network function of patients with mild cognitive impairment

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.

Examining brain structures associated with attention networks in a large sample of young adults: a voxel-based morphometry study

Attention networks have three principal com- ponents supported by separate subprocesses, which include alerting, orienting, and executive control （EC） networks. Efficiently and accurately extracting useful information from the environment as the function of attention is pivotal to our survival. Previous brain imaging studies have examined activation patterns underlying the different attention networks in different cortical regions, yet focal differences in brain structures related to attention network components were not well understood. Therefore, in this study, voxel-based morphometry was used to investigate the relationship between gray matter volume （GMV） and different attention networks in a large young adult sample （n = 156）. As a result, multiple regression analysis revealed that higher alerting scores （stronger alerting ability） were negatively significantly correlated with region gray matter volume （rGMV） cingulate cortex/precuneus）, in the PCC/PreCu （posterior which might be associated with continuous maintenance of a vigilant state. Then, lower EC scores （stronger conflict resolution ability） were associated with larger rGMV in the dorsal anterior cingu- late cortex, which might be related to high-efficiency executive control processing. Together, findings of the present study provided a unique structural basis of GMV for individual differences in alerting and EC networks.

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.