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Background Prevention and control of cardiometabolic conditions and cardiovascular disease (CVD) in China may contribute to sustainable CVD reduction globally, given the fact that one-fifth of the worldwide population is in China. Knowing the distribution of behavioral risk factors (e.g., smoking and physical inactivity), especially at a national level in China, would be extremely relevant to the field of public health and CVD prevention. The objectives of this study were to investigate the nationwide prevalence of obesity, smoking, heavy drinking, and physical inactivity in Chinese adults, and further explore whether cardiometabolic conditions would modify the distribution of behavioral risk factors. Methods This population-based study is based on the China Health and Retirement Longitudinal Study (2011–2012), including 17,302 adults (≥45 years, mean age 59.67 years, female 51.66%) from 25 provinces in China. Data on demographics, lifestyle factors, health status and history of diseases were collected via structured interviews and laboratory tests. Smoking, heavy drinking, obesity, and physical inactivity were defined following standard guidelines. We performed descriptive analysis and logistic regressions in this study. Results The overall prevalence of heavy drinking, obesity, current smoking, and physical inactivity among middle-aged and older adults was 7.23% (95% confidence interval 6.53—7.29%), 11.53% (10.43—12.62%), 27.46% (26.30—28.62%), and 44.06% (41.19—46.92%), respectively. The prevalence varied between rural and urban areas as well as among geographic areas, with higher prevalence in the Northern and Northeastern regions. Heavy drinking and obesity were significantly associated with incident hypertension, diabetes, and high cholesterol; while current smoking was significantly associated with incident hypertension. Compared with healthy individuals, participants who self-reported a diagnosis of hypertension, high cholesterol, or diabetes were less likely to smoke currently and drink alcohol heavily, but more likely to be physically inactive and obese. Conclusions Among Chinese middle-aged and older adults, the prevalence of behavioral risk factors varies by geographic region. Further effort is required to improve physical activity and fitness for Chinese adults, especially those with cardiometabolic conditions.

In order to make the dynamic equivalent method for hydropower generator group based on phasor measurement information more suitable for the actual grid. A index use tie-line power oscillation to evaluate disturbance intensity is presented .Based on this index, consider disturbance location and it’s distance to the equivalent area, a disturbance selection method is proposed. Based on the equivalent method, dynamic equivalent online identification system is developed. The disturbance selection method proposed is verified using PMU measured disturbances data of Sichuan power grid China. Introduction Along with the increase of phasor measurement device(PMU) in the real power system, dynamic equivalent for hydropower generator group based on phasor measurement information has a broad application prospect. Dynamic equivalence is one of the most traditional research direction of power system, a large amount of research on the system dynamic equivalent model and parameter identification method are studied by many scholars and achieved abundant research results. But there is a little research on the application problem of equivalent in real power system . Reference 1-2 used 39 bus system with 10 generators to verify equivalent method the paper proposed; reference 3 did research on the equivalent method application in Tianjin power grid China, but disturbance selection is not involved . Existing literatures verify their equivalent method in simulation software generally. There is little Reference studies the application of equivalent method and disturbance selection in actual power grid, which affect the application of the equivalent method and the accuracy of equivalent. Therefore, this paper researches on dynamic equivalent for hydropower generator group based on phasor measurement information of tie-line. A index use tie-line power oscillation to evaluate disturbance intensity is presented, based on this index, consider disturbance location and it’s distance to the equivalent area, a disturbance selection method is proposed. And dynamic equivalent modeling software is developed. The equivalent method and disturbance selection method proposed is verified using PMU measured disturbances data of Sichuan power grid . Example analysis shows that disturbance data selection and dynamic equivalent method are effective and feasible. The method can make hydropower generator group dynamic characteristics simulation more accurate. Review of the dynamic equivalent method According to reference 4, the basic dynamic equivalent method for hydropower generator group based on phasor measurement information proposed in this paper can be concluded as follows: 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer (MMEBC 2016) © 2016. The authors Published by Atlantis Press 2192

A novel low voltage ride-through (LVRT) scheme for PMSG-based wind turbines based on the Resistor Superconducting Fault Current Limiter (R-SFCL) is proposed in this paper. The LVRT scheme is mainly formed by R-SFCL in series between the transformer and the Grid Side Converter (GSC), and basic modelling has been discussed in detail. The proposed LVRT scheme is implemented to interact with PMSG model in PSCAD/EMTDC under three phase short circuit fault condition, which proves that the proposed scheme based on R-SFCL can improve the transient performance and LVRT capability to consolidate grid connection with wind turbines.

Hashing methods have been widely applied for fast retrieval and efficient data storage. However, most existing hashing methods have not taken the discriminative features into account in nearest neighbors search which leads to unsatisfied retrieval accuracy, especially for high-dimensional dataset. In order to take best use of discriminative information, we introduce an effective feature extraction framework for hashing that can get high retrieval accuracy in this paper. Firstly, the divergence between two classes is represented by the ratio of signal to noise. Then the discriminative features of high-dimensional data are obtained through the generalized eigen-decomposition. Finally, we exploit the discriminative feature into hashing methods to generate compact binary code. Experimental results on data sets show that the proposed framework can reach better results in comparison with state-of-the-art methods.

The dynamics of modern interconnected power system is characterized by low frequency oscillations (LFOs) which are produced as results of various disturbances such as changes in loads, tripping of lines, faults, and other discrete events. A data-driven empirical mode decomposition (EMD) method is applied to the detection of low frequency oscillation modes from disturbed trajectory with its strong non-stationary signal processing capability, but the mode mixing phenomenon serious impact on the analysis credibility and accuracy of EMD method. In this paper, an anti mode mixing EMD composite algorithm is proposed for detecting the characteristics of LFOs in power system. First, the improved frequency heterodyne method is proposed to increase the spectral distance between adjacent mode components in order to meet the octave resolution requirements. Second, the wavelet singularity detection technology is proposed to determine the adaptive sliding analysis window for each mode, in which there implements the intermittency mixed modes separation and their nonstationary parameters identification. Finally, the analysis result of interconnected grid test case verify that the proposed algorithm can effectively overcome the impact of the mode mixing existed in EMD and improve the characteristics detection accuracy of LFOs characteristics. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3134

This paper proposes a method of solving the task assignment of high-speed handling system to multiple robots. In order to reduce the state space and improve the efficiency of learning, an improved Q-learning algorithm based on peer to peer SAP is designed. In the proposed algorithm, all the Q value of the peer to peer SAP are updated after take an action, and these Q value are required only one memory unit. Compare to the standard Q-learning algorithm, the results demonstrate the efficiency of the proposed algorithm.