QIng Li

This paper focuses on performance assessment of a freight network with stochastic capacities by using reliability analysis, in which a node denotes a supplier, a transfer center, a distribution center, or a market, while an arc denotes a logistics service provider offering the freight traffic service for a pair of nodes. Due to some uncertainties in real environment, the available capacity of the provider along each arc is stochastic. Thus, network reliability that at least d units of commodity demand can be successfully delivered from the source to the destination denotes the operational performance of such a freight network. A d-minimal path–based method is developed to evaluate the freight network reliability. To advance the efficiency of solving d-minimal paths, an improved model is established by redefining capacity bounds of arcs and minimal paths. Furthermore, a new concept of expected capacity grounded on network reliability is presented to measure the service performance of the freight network. A practical case related to the coal delivery network is studied to demonstrate the implications of both network reliability and expected capacity.

We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. When there is no fixed ordering cost involved, we design a deep reinforcement learning algorithm to obtain a near-optimal ordering policy and show that there are some monotonicity properties in the learned policy. We also show that our deep reinforcement learning algorithm achieves a better performance than tabular-based Q-learning algorithms. When a fixed ordering cost is involved, we show that our deep reinforcement learning algorithm is effective and efficient, under which the problem of “curse of dimension” is circumvented.

Workplace incivility remains a prevailing issue and has significant potential for harmful consequences. This study aims to investigate the influencing mechanism of workplace incivility on work alienation from the perspective of targets. Based on the social exchange theory, our research examines the role of interpersonal trust as a mediator along with the moderator of career resilience in the said association. Through a two-wave-time-lagged quantitative research design, a sample of 315 nurses from China was investigated with questionnaires on workplace incivility, work alienation, interpersonal trust, and career resilience. The results indicated that workplace incivility was positively related to work alienation with interpersonal trust as a mediator. Workplace incivility caused a decline in interpersonal trust, which led to work alienation. Career resilience buffered such an impact. High career resilience weakened the association linking workplace incivility to interpersonal trust. Organizations should pay more attention to workplace incivility and consider empowering nurses’ career resilience, which could alleviate the negative impact of workplace incivility.

Detecting and correcting execution errors is crucial for safe and efficient goal-directed behavior. Despite intensive investigations on error processing, the cognitive foundations of this process remain unclear. Based on the presumed relation between executive attention and error processing, we implemented a seven-day EA intervention by adopting the Posner cueing paradigm to test the potential causal link from EA to error processing in healthy adults. The experimental group was trained on the Posner cueing paradigm, with a ratio of invalid cue trials to valid cue trials of 5:1 and a corresponding ratio of 1:1 in the active control group. We found that the EA intervention improved EA across intervention sessions. Critically, after the EA intervention, the HEA group showed that post-error accuracy was restored to the same level as the post-correct accuracy. However, post-error slowing and the flanker effect were not modulated by the EA intervention. Furthermore, we observed that the changes in the accuracy of VC trials positively predicted the changes in PEA and that the two groups were classified according to the changes in PEA with a 61.3% accuracy. Based on these results, we propose that EA causally drives error processing. And the capabilities of the “actively catch” more attention resources and the automatic mismatch processing developed after EA intervention is transferable to error processing, thereby directly resulting in the gains in post-error adjustments. Our work informs the potential cognitive mechanisms underlying this causal link.

Knowledge graphs entity typing aims to predict the potential types to an entity, that is,. Recently, several embedding models are proposed for KG entity types prediction according to the existing typing information of the tuples in KGs. However, most of them unreasonably assume that all existing entity typing instances in KGs are completely correct, which ignore the nonnegligible entity type noises and may lead to potential errors for the downstream tasks. To address this problem, we propose ConfE, a novel confidence-aware embedding approach for modeling the tuples, which takes tuple confidence into consideration for learning better embeddings. Specifically, we learn the embeddings of entities and entity types in separate entity space and entity type space since they are different objects in KGs. We utilize an asymmetric matrix to specify the interaction of their embeddings and incorporate the tuple confidence as well. To make the tuple confidence more universal, we consider only the internal structural information in existing KGs. We evaluate our model on two tasks, including entity type noise detection and entity type prediction. The extensive experimental results in two public benchmark datasets demonstrate that our proposed model outperforms all baselines on all tasks, which verify the effectiveness of ConfE in learning better embeddings on noisy KGs. The source code and data of this work can be obtained from https://github.com/swufenlp/ConfE.

The division of labor in the global value chain has reshaped the competitive advantage of enterprises participating in the international market and has a significant influence on the distribution of their factor income. Based on the perspective of market choice, this paper uses China’s industrial enterprises’ data, Customs Statistical Data on Import and Export, Word Input and Output Database, and BACI database from 2000 to 2007 to analyze the effect of competitive strategy of a firm’s GVC participation on its labor income share. Herein, the competition strategy is depicted by the quality and price of export goods. The empirical results show the following. Quality competition has a larger effect on labor income share than price competition. High-quality and high-price or high-quality and low-price strategies tend to have a significant negative effect on labor income share, while low-quality and low-price and low-quality and high-price strategies have a positive effect. The higher the target market GVC status, the lower the labor income share of exporting firms, and the target market GVC status amplifies that the high-quality and high-price strategies on firms’ labor income share the negative effect of high-quality and low-price strategies on exporters’ labor income share. High-quality and low-price strategies have a significant negative effect on exporters’ labor income share when competing with developing countries in developed country markets or with developed countries in developing country markets. However, the positive effect of low-quality and low-price strategies and low-quality and high-price strategies kept unchanged. The findings remain robust after controlling for endogeneity and accounting for the effects of firm heterogeneity, indicator measurement, and sample variation.

Case-cohort design is a biased sampling method. Due to its cost-effective and theoretical significance, this design has extensive application value in many large cohort studies. The case-cohort data includes a subcohort sampled randomly from the entire cohort and all the failed subjects outside the subcohort. In this paper, the adjustment for the distorted covariates is considered to case-cohort data in Cox’s model. According to the existing adjustable methods of distorted covariates for linear and nonlinear models, we propose estimating the distorting functions by nonparametrically regressing the distorted covariates on the distorting factors; then, the estimators for the parameters are obtained using the estimated covariates. The proof of consistency and being asymptotically normal is completed. For calculating the maximum likelihood estimates of the regression coefficients subject in Cox’s model, a minorization-maximization algorithm is developed. Simulation studies are performed to compare the estimations with the covariates undistorted, distorted, and adjusted to illustrate the proposed methods.

With changes in industrialization, aging population, changes in disease spectrum, ecological environment and lifestyle, in recent years, other countries or regions have begun to prioritize health in a strategic position. Since the 1990s, Taiwan, China has implemented the policy of community health building to respond to health issues caused by modern environmental pollution, work pressure, improper living and rest, bad eating habits and lack of exercise. This paper hopes to analyze the background, development process and promotion structure of community health building in Taiwan, and propose experiences that can be used for reference in other countries or in the building of healthy cities and healthy communities.