Why do authoritarian regimes provide public goods? Policy communities, external shocks and ideas in China’s rural social policy making

Recent research on authoritarian regimes argues that they provide public goods in order to prevent rebellion. This essay shows that the ‘threat of rebellion’ alone cannot explain Chinese party-state policies to extend public goods to rural residents in the first decade of the twenty-first century. Drawing on theories of policy making, it argues that China’s one-party regime extended public goods to the rural population under the influence of ideas and policy options generated by policy communities of officials, researchers, international organisations and other actors. The party-state centre adopted and implemented these ideas and policy options when they provided solutions to external shocks and supported economic development goals. Explanations of policies and their outcomes in authoritarian political systems need to include not only ‘dictators’ but also other actors, and the ideas they generate

The diagnostic analysis of the fault coupling effects in planet bearing

The purpose of this paper is to investigate the fault coupling effects in the planet bearing as well as the corresponding vibration signatures in the resultant vibration spectrum. In a planetary gear application, the planet bearing can not only spin around the planet gear axis, but also revolve about the sun gear axis and this rotating mechanism poses a big challenge for the diagnostic analysis of the planet bearing vibration spectrum. In addition, the frequency component interaction and overlap phenomenon in the vibration spectrum caused by the fault coupling effect can even worsen the diagnosis results. To further the understanding of the fault coupling effects in a planet bearing, a 34° of freedom planetary gear model with detailed planet bearing model was established to obtain the dynamic response in the presence of various bearing fault scenarios. The method of modelling the bearing distributed faults and localized faults has been introduced in this paper, which can be further incorporated into the planetary gear model to obtain the faulted vibration signal. The “benchmark” method has been adopted to enhance the planet bearing fault impulses in the vibration signals and in total, the amplitude demodulation results from 20 planet bearing fault scenarios have been investigated and analyzed. The coherence estimation over the vibration frequency domain has been proposed as a tool to quantify the fault impact contribution from different fault modes and the results suggested that the outer raceway fault contributes most to the resultant planet bearing vibration spectrum in all the investigated fault scenarios

The diagnostic analysis of the planet bearing faults using the torsional vibration signal

© 2019 Elsevier Ltd This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planet bearing fault detection. The inner race of the planet bearing is connected to the planet carrier and its outer race is connected to the planet gear bore hole. When moving, the planet bearing not only spins around the planet gear axis, but also revolves about the sun gear axis. This rotating mechanism poses a challenge for the condition monitoring of the planet bearing because of the variant vibration transfer paths. The transducer mounted on the carrier arm measuring the torsional vibration is theoretically free from this modulation effect and it is used in this research to extract the diagnostic information from the torsional vibration. A 34 degrees of freedom planetary gear lumped-parameter model with detailed planet bearing model was developed to obtain the dynamic response. The planet bearing was modelled by 5 degrees of freedom, with 2 degrees of freedom from the outer race, 2 degrees of freedom from the inner race and one degree of freedom from the sprung-mass. The variations of the sun-planet and ring-planet mesh stiffnesses were evaluated by the finite element method and the variation of the planet bearing stiffness was evaluated by the Hertzian contact theory. The localized faults on the planet bearing inner race, outer race and the rolling element were created mathematically and then these faults were incorporated into the planetary gear model to obtain the faulted vibration signal. The linear prediction method and the minimum entropy deconvolution method were used to enhance the planet bearing signal and then the amplitude demodulation results were analysed. It was found that the carrier arm instantaneous angular speed was an effective alternative approach for planet gear condition monitoring

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Subway Sliding Plug Door System Health State Adaptive Assessment Method Based on Interval Intelligent Recognition of Rotational Speed Operation Data Curve

The subway sliding plug door system is crucial for ensuring normal operation. Due to the differences in the structure and motor control procedures of different sliding plug door systems, the rotational speed monitoring data curves show great differences. It is a challenging problem to recognize the intervals of complex data curves, which fundamentally affect the sensitivity of feature extraction and the prediction of an assessment model. Aiming at the problem, a subway sliding plug door system health state adaptive assessment method is proposed based on interval intelligent recognition of rotational speed operation data curve. In the proposed method, firstly, the rotational speed operation data curve is adaptively divided by a long short-term memory (LSTM) neural network into four intervals, according to the motion characteristics of the door system. Secondly, the sensitive features of the door system are screened out by the random forest (RF) algorithm. Finally, the health state of the door system is assessed using the adaptive boosting (AdaBoost) classifier. The proposed method is comprehensively verified by the benchmark experiment data set. The results show that the average diagnostic accuracy of the method on multiple bench doors can reach 98.15%. The wider application scope and the higher state classification accuracy indicate that the proposed method has important engineering value and theoretical significance for the health management of subway sliding plug door systems

Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation -driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the eval-uation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention.Funding Agencies|Youth Innovation Team of Shaanxi Universities ?; Special ConstructionFund for Key Disciplines of Shaanxi Provincial Higher Education; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; Shaanxi Provincial Education Department [22JK0567]; Project of National Natural Science Foundation of China [62271390, 51905399]; Postgraduate Innovation Fund of Xian University of Posts and Telecommunications [CXJJDL2022012]</p