The Influence of Education and Scientific Research System on China's Science and Technology Innovation Capability

This article outlines their impact on China's technological innovation capabilities from nine aspects including primary and secondary education to university education, the shortcomings of scientific research evaluation system, the forward-looking of educational investment and the rationality of research funding, the negative feedback of the employment market on innovative research, intellectual property protection and incentive mechanism, The basic social system and its incentive mechanism combined with learning and research, the incentive mechanism and cultural atmosphere of enterprises and administrative institutions, and the origin of China's modern education model. The comprehensive analysis shows that changing the status quo of China's lack of innovation is a systematic project. A single ministry cannot complete many specific measures of reform, and must have a national-level top-level design. Through reform, the education and scientific research system has reasonable design and strong self-repairing ability. It is the need of innovation to promote industrial upgrading. Its effectiveness directly determines whether China can cross the middle income trap and the great rejuvenation of the Chinese nation

Application of Graphene in Coatings: A Survey

Graphene has been applied and demonstrates its excellent functions in various functional coatings by virtue of its excellent thermal, mechanical and electrical properties. This paper mainly introduces the application status and effect of graphene in conductive coating, anticorrosive coating, flame retardant coating, thermal conductive coating and high-strength coating. Finally, the application prospect of graphene in the field of coating is prospected

Comparing public and private hospitals in China: Evidence from Guangdong

<p>Abstract</p> <p>Background</p> <p>The literature comparing private not-for-profit, for-profit, and government providers mostly relies on empirical evidence from high-income and established market economies. Studies from developing and transitional economies remain scarce, especially regarding patient case-mix and quality of care in public and private hospitals, even though countries such as China have expanded a mixed-ownership approach to service delivery. The purpose of this study is to compare the operations and performance of public and private hospitals in Guangdong Province, China, focusing on differences in patient case-mix and quality of care.</p> <p>Methods</p> <p>We analyze survey data collected from 362 government-owned and private hospitals in Guangdong Province in 2005, combining mandatorily reported administrative data with a survey instrument designed for this study. We use univariate and multi-variate regression analyses to compare hospital characteristics and to identify factors associated with simple measures of structural quality and patient outcomes.</p> <p>Results</p> <p>Compared to private hospitals, government hospitals have a higher average value of total assets, more pieces of expensive medical equipment, more employees, and more physicians (controlling for hospital beds, urban location, insurance network, and university affiliation). Government and for-profit private hospitals do not statistically differ in total staffing, although for-profits have proportionally more support staff and fewer medical professionals. Mortality rates for non-government non-profit and for-profit hospitals do not statistically differ from those of government hospitals of similar size, accreditation level, and patient mix.</p> <p>Conclusions</p> <p>In combination with other evidence on health service delivery in China, our results suggest that changes in ownership type alone are unlikely to dramatically improve or harm overall quality. System incentives need to be designed to reward desired hospital performance and protect vulnerable patients, regardless of hospital ownership type.</p

Decision Model of the Best Investment Opportunity in Coal Mine Project Based On Real Option

The traditional decision-making methods of investment decision about coal mine projects have many deficiencies. Decision model of coal mine project option is proposed in paper . Firstly, analysis the characteristics which include uncertainty and irreversibility of physical option and build a suitable model after researching the significance ,then decide the best time though the coal mine project. Finally, case analysis presents the application of this method and provides strong theoretical basis for decision in coal mine project. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2857

Evolutionary Game Analysis of the Supervision Behavior for Public-Private Partnership Projects with Public Participation

The public can directly or indirectly participate in the PPP (public-private partnership) projects and then has an impact on the project profit and public or private behavior. To explore the influence of the public participation of the PPP projects supervision behavior, this paper analyzes the mutual evolutionary regularity of the private sector and government supervision department and the influence of public participation level on public and private behavior based on evolutionary game theory. The results show that the supervision strategy is not chosen when the supervision cost of government supervision department is greater than the supervision benefit; it can make private sector consciously provide the high-quality public products/services with the improvement of public participation level. Therefore, the government should reduce the cost of public participation and improve the public participation level and influence through the application of the Internet, big data, and other advanced technologies, in order to restrain the behavior of the private sector and improve the supervision efficiency

Evolutionary Game Analysis of the Supervision Behavior for Public-Private Partnership Projects with Public Participation

The public can directly or indirectly participate in the PPP (public-private partnership) projects and then has an impact on the project profit and public or private behavior. To explore the influence of the public participation of the PPP projects supervision behavior, this paper analyzes the mutual evolutionary regularity of the private sector and government supervision department and the influence of public participation level on public and private behavior based on evolutionary game theory. The results show that the supervision strategy is not chosen when the supervision cost of government supervision department is greater than the supervision benefit; it can make private sector consciously provide the high-quality public products/services with the improvement of public participation level. Therefore, the government should reduce the cost of public participation and improve the public participation level and influence through the application of the Internet, big data, and other advanced technologies, in order to restrain the behavior of the private sector and improve the supervision efficiency

DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network

Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation systems. The results in previous research, however, suffer from problems such as data compression, information damage, and insufficient learning. Therefore, a DeepFM Graph Convolutional Network (DFM-GCN) model was proposed to alleviate the above issues. The prediction of the click-through rate (CTR) is critical in recommendation systems where the task is to estimate the probability that a user will click on a recommended item. In many recommendation systems, the goal is to maximize the number of clicks so the items returned to a user can be ranked by an estimated CTR. The DFM-GCN model consists of three parts: the left part DeepFM is used to capture the interactive information between the users and items; the deep neural network is used in the middle to model the left and right parts; and the right one obtains a better item representation vector by the GCN. In an effort to verify the validity and precision of the model built in this research, and based on the public datasets ml1m-kg20m and ml1m-kg1m, a performance comparison experiment was designed. It used multiple comparison models and the MKR and FM_MKR algorithms as well as the DFM-GCN algorithm constructed in this paper. Having achieved a state-of-the-art performance, the experimental results of the AUC and f1 values verified by the CTR as well as the accuracy, recall, and f1 values of the top-k showed that the proposed approach was excellent and more effective when compared with different recommendation algorithms

Mechanism of User Participation in Co-creation Community: A Network Evolutionary Game Method

Active participation closely associates with the sustainable operation of co-creation communities. Different from recent studies on the promotion of sustainable operation by identifying the internal and external motivations of user participation, this paper aims to analyze the mechanism regarding how different motivations affect the decision of user participation from group-level perspective. To better understand the mechanism, internal and external motivations are, respectively, captured by return-cost analysis and user interactive network. Afterwards, a network evolutionary game model was formulated to analyze the dynamic strategy selection (e.g., active participation and passive participation) of all users. In addition, the stable equilibrium and evolutionary path of strategies are analyzed through computational experiments. Results indicate the following: (a) Rewards have an influence on the promotion of active participation. However, with the continued growth of rewards, this promotion does not make sense sustainably. (b) The promotional effect of information noise on the selection of active participation can be found when passive participation is the dominant strategy. However, the inhibitory effect can be seen in populations that mainly adopt active participation. (c) The scale-free feature of user interactive network inhibits the selection of active participation when active participation is the dominant strategy in populations. Results found here is beneficial for managers to implement the specified policies and thus to achieve the sustainability of co-creation community

DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network

Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation systems. The results in previous research, however, suffer from problems such as data compression, information damage, and insufficient learning. Therefore, a DeepFM Graph Convolutional Network (DFM-GCN) model was proposed to alleviate the above issues. The prediction of the click-through rate (CTR) is critical in recommendation systems where the task is to estimate the probability that a user will click on a recommended item. In many recommendation systems, the goal is to maximize the number of clicks so the items returned to a user can be ranked by an estimated CTR. The DFM-GCN model consists of three parts: the left part DeepFM is used to capture the interactive information between the users and items; the deep neural network is used in the middle to model the left and right parts; and the right one obtains a better item representation vector by the GCN. In an effort to verify the validity and precision of the model built in this research, and based on the public datasets ml1m-kg20m and ml1m-kg1m, a performance comparison experiment was designed. It used multiple comparison models and the MKR and FM_MKR algorithms as well as the DFM-GCN algorithm constructed in this paper. Having achieved a state-of-the-art performance, the experimental results of the AUC and f1 values verified by the CTR as well as the accuracy, recall, and f1 values of the top-k showed that the proposed approach was excellent and more effective when compared with different recommendation algorithms

A Multi-party Decision Hot-standby Model

The dual server hot-standby mechanism is often used to improve the system availability. However, in traditional dual server hot-standby models, the states of servers are seldom determined from the client’s observation, and it’s easy for the master server and the slave server to make wrong decisions about the state of each other, which may cause split brain. This paper presents a multi-party decision hot-standby model. In this model, the master server and the slave server determine the state of each other not only from the observation of themselves, but also from the observation of the client, which helps them make correct decision to maintain or change the service platform, so as to ensure the continuity of application. Compared with traditional dual server hot-standby models, the model suggested in this paper is more reasonable because of the involvement of the client’s observation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.488