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The infrastructure investment is one important source of economic growth in China in the past three decades. However it is not clear to what extent such investment affects development in rural area. The purpose of this paper is to explore this impact both conceptually and quantitatively, and draw policy implications from the empirical exercise.

The authors first describe the conceptual link between the transportation infrastructure and rural development, which motivates the empirical model. Then by utilizing an autoregressive distributed lag model, the authors estimate both the short- and long-run impacts of the transportation infrastructure on rural development, in terms of cereal yield and per capita net income of rural households.

The authors find that investment in transportation infrastructure positively affects rural development in China. In terms of cereal yield, a 1 percent increase in the road infrastructure (road length) leads to around 0.05 percent increase in cereal yield in the short-, and around 0.19 percent increase in the long-run. In terms of the per capita net income of rural households, a 1 percent increase in the road infrastructure results in around 0.14 percent increase in the short-, and its long-run impact is not statistically significant. The positive impacts lend supports to promote investment in the transportation infrastructure. To this end, in addition to the government funding, the participation of private capital can also be promoted through a number of channels, such as the build-operate-transfer, public-private partnership, and establishment of infrastructure investment bank.

This study evaluates the impacts of transportation infrastructure on rural development in China. Despite of the importance of infrastructure and rural development, there is a lack of study on the interaction between them. This paper intends to fill in this gap. In addition, implications drawn in this exercise can benefit policy makers not only in China, but also in other developing countries.

This paper aims to deal with the trade-off of the stability and the accuracy in learning human control strategy from demonstrations. With the stability conditions and the estimated stability region, this paper aims to conveniently get rid of the unstable controller or controller with relatively small stability region. With this evaluation, the learning human strategy controller becomes much more robust to perturbations.

In this paper, the criterion to verify the stability and a method to estimate the domain of attraction are provided for the learning controllers trained with support vector machines (SVMs). Conditions are formulated based on the discrete-time system Lyapunov theory to ensure that a closed-form of the learning control system is strongly stable under perturbations (SSUP). Then a Chebychev point based approach is proposed to estimate its domain of attraction.

Some of such learning controllers have been implemented in the vertical balance control of a dynamically stable, statically unstable wheel mobile robot.

The purpose of this paper is to improve the precision of gangue detection. In the real production environment, some gangue features are not obvious, and it is difficult to distinguish between coal and gangue. The color of the conveyor belt is similar to the gangue, the background noise also brings challenge to gangue detection. To address the above problems, we propose a feature aggregation method based on optical flow (FAOF).

An FAOF is proposed. First, to enhance the feature representation of the current frame, FAOF applies the timing information of video stream, propagates the feature information of the past few frames to the current frame by optical flow. Second, the coordinate attention (CA) module is adopted to suppress the noise impact brought by the background of convey belt. Third, the Mish activation function is used to replace rectified linear unit to improve the generalization capability of our model.

The experimental results show that the gangue detection model proposed in this paper improve 4.3 average precision compared to baseline. This model can effectively improve the accuracy of gangue detection in real production environment.

The key contributions are as follows: this study proposes an FAOF; this study adds CA module and Mish to reduce noise from the background of the conveyor belt; and this study also constructs a large gangue data set.

Aiming at the inefficiency of solving the Sobol index using the traditional mathematical analytical method, a Sobol global sensitivity analysis method is proposed.

In this paper, a support vector regression (SVR) surrogate model is constructed to solve the Sobol index. The optimal combination of SVR hyperparameters is obtained by using the improved beluga whale optimization (IBWO). Meanwhile, in order to solve the problem that Sobol sequences will form correlation regions in high-dimensional space leading to the uneven distribution of sampling points, a scrambled strategy is introduced in the Sobol sensitivity analysis using IBWO-SVR. Thus, the IBWO-SVR-SS sensitivity analysis model is established.

The results of two test functions show that the method further improves the accuracy of the sensitivity analysis. Finally, the first-order Sobol index and second-order Sobol index are solved by the IBWO-SVR-SS method using the metro bogie frame as an engineering example. Through the analysis results, the key design parameters of the frame and the design parameter combinations with more obvious coupling relationships are identified, providing a strong reference for the subsequent analysis and structural optimization.

Sobol sensitivity analysis using the surrogate model method can effectively improve the efficiency of the solution. In addition, IBWO is used for the optimization of the SVR hyperparameters to improve the accuracy and efficiency of the optimization, and finally, the correction of the Sobol sequence through the introduction of the disruption strategy also further improves the accuracy of the sensitivity analysis of Sobol.

Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

This paper explores the mechanism of organizational Pao culture in an Oriental cultural context and its impact on CSR implementation and outcomes.

Three short cases are presented to reveal how the traditional Chinese Pao culture influences CSR implementation and outcomes in Chinese firms.

The findings suggest that the traditional Chinese Pao culture is the driver behind the value orientation of the norms of passing on favors and doing good deeds will be rewarded. Knowing this can help leaders in such organizations to incentivize member participation in building organizational Pao culture.

Through the collaborative construction of the core of Pao culture and external evaluation standards of corporate social responsibility, enterprises can thus achieve the dual goals of self-development and social benefits.

Enterprises and managers can be encouraged to draw management wisdom from the essence of cultural norms, so as to achieve the goal of realizing the interests of all CSR participants.

This paper emphasizes the dynamics and outcomes of CSR implementation in the context of the traditional Chinese Pao culture and expands the boundary of CSR research beyond a Western cultural setting.

Research on entrepreneurship toward poverty reduction has outlined how micro-level characteristics of entrepreneurs capture entrepreneurial opportunities in settings of poverty; however, little is known about the influence of previous military experience in this context. This paper investigates how previous military experience influences poverty-reduction entrepreneurship.

This study uses data from two main sources. First, individual-level and firm-level information come from a nationwide survey of founders of private enterprises. Second, province-level information is taken from the Marketization Index and the China Statistics Yearbook. An analysis of the Logit moderation model renders strong support for our conjectures.

Via novel integration of imprinting theory and research on previous military experience, we propose that entrepreneurs with previous military experience have a strong sense of self-sacrifice, and as a result, are better able to participate in poverty-reduction entrepreneurship. In addition, we build on the resource availability and stakeholder expectations arguments and predict that the main effect of previous military experience on poverty-reduction entrepreneurship will be strengthened by reduced corporate philanthropy and increased government intervention.

Our study adds to the extant literature in the following ways. First, it enriches the literature on entrepreneurship toward poverty reduction. Second, it contributes to imprinting theory in the entrepreneurial field. Third, it adds knowledge to the social entrepreneurship literature.

This paper aims to deal with the coordination problem of the supply chain through cost sharing of corporate social responsibility (CSR) and government subsidy.

With respect to the coordination problem of the supply chain with CSR, this paper constructs a three-stage game model consisting of a dominant retailer, n suppliers and government. From the perspective of cost sharing and government subsidies, this paper discussed the decentralized and centralized decision-making, respectively. On this basis, this paper designed a coordination mechanism considering both cost sharing and government subsidies and explore the impact of cost sharing rate and government subsidy rate on CSR efforts, members’ profits and social welfare.

CSR can improve the profits of supply chain members and the overall performance of the supply chain. Then the profits of supply chain nodal enterprises will be affected by the fulfillment level of CSR of their partners. Furthermore, excessive CSR will erode the supply chain profits and cause resource waste. High CSR costs often make retailers low CSR effort level, while a high CSR cost sharing rate can reduce the profits of suppliers and the supply chain. In addition, excessive government subsidies will lead to the decline of social welfare. Excessive government subsidies will cause the dependence of enterprises and affect their operating efficiency.

The proposed coordination mechanism can effectively do with the coordination problem of the supply chain.

The proposed coordination mechanism considering cost sharing and government subsidies simultaneously can effectively deal with conflict problems and guarantee the supply chain members and the supply chain to maximize their profits and social welfare.

The purpose of this paper is to estimate the cost and profit efficiency (PE) of Chinese commercial banks in the last ten years and investigate how market power affects bank efficiency and stability.

The paper builds a stochastic frontier analysis model to evaluate the cost and PE of commercial banks. The paper then uses a Lerner index and Z-index to represent market power and stability, respectively. In addition, the paper empirically analyzes the relationship between market power and bank efficiency, stability in the last ten years.

The results show that the efficiency of banks on the Chinese mainland increased during the study period, but is still lower than that of banks in Hong Kong; moreover, the efficiency of four state-owned commercial banks is lower than that of medium and small banks. Market power has a negative relationship with efficiency while its relationship with stability varies among Chinese banks.

The results imply that the promotion of financial liberalization and banking reform to introduce an appropriate competition mechanism has had a positive effect on the efficiency and stability of Chinese commercial banks.

Thus, the paper will contribute to deepen reform and opening up the banking sector in China.

The healthy development of banking can enhance the ability of banks to withstand financial risks, to promote the harmonious development of society.

The paper estimates the cost and PE of Chinese commercial banks using SFA model and investigates how market power affects bank efficiency and stability. The study design has a certain novelty, where Lerner index and Z index are used, respectively, to measure market power and stability and management efficiency of commercial banks is investigated from two aspects – PE and cost efficiency – by the translog cost function, instead of Douglas production function. In addition, the paper tries to put some of Hong Kong banks included in the study sample, and has a certain reference value.