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Supply chain finance (SCF) plays an increasingly important role in global enterprise competition. The credit risk accompanying SCF has attracted the attention of the government, enterprises, and academia. However, with the absence of data and inaccurate information, traditional risk assessment methods are frequently failed to assess the credit risk in SCF, especially for small- and medium-sized enterprises (SMEs). In this study, a grey correlation model is introduced and applied to the SCF risk assessment process for 15 firms in the Chinese home appliance industry with 15 performance indicators that represent profitability, solvency, operational capability, and development capability. The empirical study displays the operability and effectiveness of the grey correlation model, which is superior to traditional methods in the supply chain financial risk assessment.

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Attention bias modification training in individuals with depressive symptoms: A randomized controlled trial.

In the current context of the establishment of world-class universities and disciplines in China, this study examined the investment of research funds at universities. First, six variables were selected as evaluation indicators from the perspective of fixed assets, teaching configuration, research instruments, and the number of books in libraries. Seventy-two universities were investigated from 2013 to 2017. Second, an evaluation system was constructed using the BP (backpropagation) neural network method and its applicability was verified. Finally, by adjusting the six indicators, the investment of university research funds could be adjusted and predicted to provide a reference for the construction of “first-class” universities and disciplines.

The gray prediction model, based on the GM(1,1) method, is an important branch of gray theory with the most active research and the most fruitful results, and it is the most widely used because of its small sample size, simple modeling process, and easy to use. Such advantages have been successfully applied in many fields such as transportation, agriculture, energy, medicine, and environment and have been gradually developed into a mainstream predictive modeling method. This study combines the Three-parameter Whitenization Grey Model (TWGM(1,1)), which fits the inhomogeneous exponential law sequence, and the Particle Swarm Algorithm (PSA) to optimize the order and background value coefficients under the condition of the minimum sum of squares of simulation errors, and hence, to solve the problem that the cumulative order is fixed to “1” and the background value coefficient value is fixed to “0.5.” As a result, a parameter-optimized gray system model with flexibility, adaptability, and dynamic adjustment is designed to simulate and predict China’s higher education gross enrollment rate. The application shows that the model has better overall simulation and prediction performance than others. On the one hand, the parametric optimization model significantly improves its own performance, and on the other hand, its intelligent and adjustable adaptivity improves the accuracy and further extends its application.

BackgroundAlthough previous studies have explored the moderating role of emotional regulation strategies in the relationship between empathy and depression, no studies have studied the moderating role of attentional control in the relationship between empathy and depression. To address this research gap, the present study investigated the moderating roles of rumination and attentional control in the relationship between empathy and depression.Methods423 participants filled out questionnaires anonymously, including Interpersonal Reactivity Index, Attention Control Scale, Self-rating Depression Scale, and Rumination Response Scale. PROCESS macro for SPSS was used for moderating effect analysis.ResultsRumination and attentional shift moderated the relationship between emotional empathy and depression. Specifically, the lower rumination or the higher attentional shift, the stronger the negative association between emotional empathy and depression. Attentional shift moderated the relationship between cognitive empathy and depression, and cognitive empathy was significantly associated with depression only among participants whose attentional shift is high.ConclusionThe study showed that rumination and attentional shift play important roles in the relationship between empathy and depression. The findings implicated that the positive role of good emotional regulation strategies and executive function for individuals in the relationship between empathy and depression.