China has become the major stakeholder in global photovoltaic (PV) technology. However, the existing mechanistic interpretation of “what promotes the technological progress of the Chinese PV industry” is controversial. This paper takes China’s A-share listed PV enterprises from 1999 to 2019 as the research sample and uses a panel fixed-effect regression model to empirically test the impact of research and development (R&D) investment and government subsidies on the technological progress of PV enterprises. The results show that there is an “N”-shaped nexus between R&D investment and technological progress, and most PV enterprises are in the climbing stage of the N-shaped curve. With the development of the PV industry, the nexus will undergo a transformation from inverted U-shaped to N-shaped, indicating that R&D investment is a key driver of PV technological progress. Yet, government subsidies are a “double-edged sword”. They have a significant positive direct effect on PV technological progress but also a negative moderating effect. Tax returns play a positive incentivizing role, while financial subsidies play a negative moderating role. This study provides a policy basis for the timely reduction of financial subsidies and increased R&D investment to promote technological progress in China’s PV industry.

Since the outbreak of the coronavirus disease (COVID-19) in 2019, caused by SARS-CoV-2, the disease has become a global health threat due to its high infectivity, morbidity, and mortality rates. With China’s comprehensive relaxation of pandemic control policies in 2022, the risk of infection for nursing personnel has further increased. This study aims to identify risk factors associated with depression among nursing staff during the full reopening of COVID-19 in China in 2022 and to construct a predictive model to assess the risk. From December 9, 2022, to April 6, 2023, a cross-sectional study was conducted in three hospitals in Anhui Province, including 293 nursing staff. The research subjects were divided into a depression group and a non-depression group, and SPSS 23.0 software was used to analyze the data of both groups. We developed four predictive machine learning models: logistic regression, support vector machine, extreme gradient boosting machine (XGBoost), and adaptive boosting (AdaBoost). The development and validation of these models utilized open-source Python libraries such as Scikit-learn and XGBoost. The models were trained and validated using a 10-fold cross-validation method, and the final model selection was based on the area under the receiver operating characteristic curve (AUC). The AUC values for the logistic regression, SVM, Logistic, XGBoost, and AdaBoost models were 0.86, 0.88, 0.95, and 0.93, respectively, with F1 scores of 0.79, 0.83, 0.90, and 0.89, respectively. The XGBoost model demonstrated the highest predictive accuracy. However, the study’s findings are limited by the small sample size and single location, and further validation is needed to confirm the model’s generalizability. The extreme gradient boosting machine model, tailored for common risk factors among Chinese nursing staff, provides a powerful tool for predicting the risk of depression. This model can assist clinical managers in accurately identifying and addressing potential risk factors during and after the full reopening of COVID-19. Since the working environment and stress factors faced by nursing staff may vary across different countries, the research findings from China can promote international exchange and cooperation in the management of mental health among nursing staff, advice future research should focus on larger, multi-center studies to validate the model’s performance and explore additional risk factors. Not applicable, because of this article belongs to cross-sectional study.

Purpose: In this paper, innovative proposes the concept of integrating green delivery, incorporating post-manufacturing requirements into the development process of aviation equipment products. Design/methodology/approach: Through Stackelberg game equilibrium analysis and case validation, we investigate the impact of green innovation capability elasticity on the effectiveness of cost-sharing incentives. Findings: When the elasticity of green (assembly) process innovation capability is low, cost-sharing by the main manufacturer can better incentivize the green delivery process. As elasticity increases but remains within a controllable range, non-cost-sharing incentives become more effective. Originality/value: By optimizing cost-sharing incentive mechanisms, main manufacturers can more effectively guide integrated suppliers to participate in green delivery, thereby reducing environmental impact and improving overall supply chain efficiency. Additionally, this research provides a reference framework for green supply chain management in other high-end equipment manufacturing industries, contributing to their sustainable development.

Identifying and assessing the key factors that influence the operations of public transportation from an operational risk perspective is a critical challenge. In this paper, in order to tackle this issue by dissecting 18 risk factors through a comprehensive lens that encompasses five dimensions of the public transportation operation: infrastructure completeness, operational procedures, management practices, process requirements, and risk mitigation strategies, each with its respective evaluative metrics. To offer a nuanced analysis of these risk factors, we introduce an entropy weight-based DEMATEL (Decision-Making Trial and Evaluation Laboratory) method. This approach leverages the entropy weight decision-making process to quantify the significance of each risk factor, acknowledging the inherent uncertainties in the evaluation process. By applying the DEMATEL method to the collected data, it becomes evident that safety consciousness stands out as a pivotal factor, with human initiative emerging as a central element in the operational flow of public transportation. Furthermore, our study reveals that substantial funding, technological advancements, regular inspections, and stringent regulations are critical risk factors that warrant considerable attention in the management of public transportation operations. The findings underscore the necessity for a multifaceted approach to risk management to bolster the safety and efficiency of public transportation services. To foster a safer and more effective public transportation system, it is imperative for stakeholders to not only heighten their vigilance regarding safety but also to ensure that adequate financial resources and regulatory frameworks are in place. Only through a holistic and multidimensional risk management strategy can we hope to minimize the incidence of public safety risks.