Anhui University of Finance and Economics

ESG rating divergence is a reflection of the different approaches taken by different agencies to assess a company's ESG effectiveness, which could influence the stock market's precise valuations and pricing efficiency. This article tested the aforementioned effects using Chinese A-listed firms. Our research indicates that ESG rating divergence significantly increases stock market mispricing. The mechanism is that investor disagreement and investor emotional fluctuations are made worse by ESG rating divergence, hence increasing stock market mispricing. Further analysis suggests that ESG rating divergence causes the company's stock to be undervalued. Cross-sectional data demonstrates that the impact of ESG rating divergence on stock market mispricing is particularly noticeable when listed firms have significant financing constraints, large analyst earnings forecast deviations, and low levels of free float shares. Our study provides empirical proof that ESG rating divergence leads to stock market mispricing and supports the idea of the "noise effect".

In the fast-growing new energy vehicle (NEV) industry, selecting an appropriate technological innovation strategy is vital for enterprises to achieve a competitive market position while effectively coordinating their resources to align with their technical capabilities. This paper integrates ambidextrous innovation theory and the resource-based view to propose a configurational model that examines how the synergy between technological innovation and resources influences NEV firm performance. Using regression analysis and qualitative comparative analysis (QCA) for 52 listed Chinese NEV companies, this study uncovered multiple growth paths and mechanisms. The findings include the following: (1) No single factor was a necessary condition for performance, but effective combinations of innovation strategies and resource elements led to multiple success paths. (2) Government subsidies and R&D investment emerged as key drivers of performance. (3) Four distinct configuration paths were identified, with variations across firms with different resource bases. (4) In response to reduced government subsidies, NEV firms must shift from policy-driven strategies to resource- and market-driven innovation approaches. These insights provide strategic guidance for NEV enterprises in selecting innovation strategies suited to their unique resource bases in the evolving post-subsidy market environment.

This study focused on the intricate connections between hypertension nephropathy (HN) and diabetic nephropathy (DN) in terms of molecular and pathological mechanisms. The samples were from the Gene Expression Omnibus (GEO) database. GSE37460 and GSE142153 are training sets, and GSE37455 and GSE30529 are validation sets. We found 42 shared differentially expressed genes (DEGs) by means of the differential analysis. The GO/KEGG and GSEA analysis mainly highlights the signal transduction pathways related to the proteasome and cytokines. The eight hub genes identified through the Protein-Protein Interaction (PPI) network analysis include NR4A1, TNFSF10, CX3CR1, EGF, THBD, CXCR4, CCL5, and ATF3. Single-cell sequencing analysis revealed that TNFSF10 and NR4A1 were the most highly expressed in the cells of both HN and DN. Furthermore, five significant microRNAs identified include hsa-miR-1248, hsa-miR-200b-5p, hsa-miR-23b-5p, hsa-miR-3059-5p, and hsa-miR-3065-3p. Six essential transcription factors (TFs) (NFIL3, STAT3, NFKB1, USF1, USF2, and EGR1), 11 important drug chemicals (Cisplatin, Cyclosporine, perfluorooctanoic acid, Quercetin, Tretinoin, bisphenol A, Curcumin, Valproic Acid, Particulate Matter, Simvastatin, and Cadmium), seven related diseases (Atherosclerosis, Glioblastoma, Pulmonary Fibrosis, Asthma, Hepatitis B, Hepatitis C, and Diabetes Mellitus), and ten important RNA-binding proteins (RBPs) (CHTOP, EIF4E, HNRNPK, IGF2BP3, YTHDF3, HNRNPA2B1, RBM47, YBX1, RBFOX2, and RBM10). Finally, molecular docking simulations suggest that Tretinoin and Curcumin may have potential therapeutic value for both HN and DN. This study provides novel therapeutic targets for the combined diagnosis and treatment of HN and DN. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-04679-w.

Textile tow defect detection faces challenges such as slow speed, small object sizes, and low accuracy. To address these issues, we propose PRD-YOLOv11, a model that improves accuracy while maintaining speed. A key innovation is the Progressive Feature Compression Downsampling (PFCD) method, which uses layered compression and decompression to reduce parameters and memory usage while preserving semantic information. PFCD includes two versions: Complex PFCD (CPFCD) for shallow feature extraction and Simple PFCD (SPFCD) for deep feature extraction, balancing efficiency and representation. Additionally, the Region-Guided Dynamic Attention (RGA) mechanism enhances feature extraction by adaptively allocating weights and aggregating context features, improving robustness in complex backgrounds. We also introduce Dual-Stream Loss (DS) to accelerate convergence and enhance detection capabilities beyond traditional IoU metrics. Experiments on our custom Cellulose Tow (CT) dataset show PRD-YOLOv11 achieves an mAP of 69.3% (a 4.7% improvement) and F1 score of 70.2% (a 6.6% improvement), with only a 0.3 GFLOPs increase, and a real-time detection speed of 47 FPS. To further validate the model’s generalization ability, we tested it on public datasets NEU, it achieves mAP scores of 80.7%, demonstrating superior performance.

The outbreak and rapid spread of the COVID-19 in December 2019 (Iqbal Z, Aslam MZ, Aslam T, Ashraf R, Kashif M, Nasir H, Register J, 2020, 13, 208–30) has brought great work pressure to nurses on the frontline of the fight against the virus, which is very likely to lead to work deviant behaviors, therefore, how to effectively manage nurses to inhibit their organizational deviance in the context of an emergency public health crisis has a high research value. A questionnaire was administered to 319 Chinese in-service nurses, and SPSS and AMOS software were used to conduct correlation analysis, confirmatory factor analysis, and hierarchical regression analysis to statistically test the hypotheses of the developed model. COVID-19 stress can significantly positively predict nurses’ organizational deviance. The relationship between the two variables is mediated by job satisfaction. Furthermore, perceived organizational support(POS) demonstrates a dual moderating function in our framework: it not only influences the relationship between CST and employee job satisfaction, but also affects the extent to which satisfaction mediates subsequent organizational outcomes. COVID-19 stress is an important psychological factor influencing nurses’ organizational deviance. The government and relevant organizations are supposed to take the psychological stress of such primary medical staff seriously, provide more supportive resources and take various measures to reduce COVID-19 stress to help individuals cope with the COVID-19 crisis.

As China’s economy transitions into a stage of high-quality development, the shift from a factor-driven growth model to an innovation-driven one has become crucial. This paper utilizes panel data from 258 cities in China spanning from 2004 to 2019 to investigate the impact of growth targets at different stages of economic development on regional talent allocation. Additionally, it explores whether long-term innovation-driven strategies can counterbalance the imbalances in regional talent allocation caused by short-term growth target constraints. The findings of this study are as follows: (1) Economic growth target constraints significantly hinder regional talent allocation, with particularly pronounced effects in non-capital and non-innovative cities. Conversely, innovation-driven approaches substantially enhance talent allocation efficiency, demonstrating stronger impacts in non-capital cities and innovation hubs. (2) Mechanistic analysis reveals dual pathways: Growth targets exacerbate local protectionism and market fragmentation, thereby impeding marketization processes and talent mobility. In contrast, innovation-driven strategies foster industrial agglomeration upgrading, creating self-reinforcing cycles for talent concentration. (3) Moreover, the impact of innovation-driven on regional talent allocation is influenced by the intensity of economic growth target constraints, showing a threshold effect. Specifically, the higher the intensity of local economic growth target constraints, the smaller the effect of innovation-driven on regional talent allocation.

Background As China undergoes rapid modernization concurrent with accelerated aging, older adults are exposed to multifaceted cultural dynamics. Consequently, employment may carry multidimensional significance as a status passage. This study aimed to investigate the impact of employment on the mental health of elderly persons in China, leveraging middle-range theory to understand the nuanced social significance of employment as a status passage. Methods This paper selected older adults aged 60 to 75 years from three waves (2015, 2018, and 2020) of the China Health and Retirement Longitudinal Study (CHARLS) survey data as the analytical cohort. The analytical approach involved fixed effects models for the core empirical analysis, propensity score matching (PSM) to address selection bias, and instrumental variable techniques to tackle reverse causality. Results The study demonstrates that employment retains positive properties of status passage for Chinese older adults in multicultural contexts, with empirical evidence showing significant reduction in depressive symptoms (β = −0.3945, p < 0.01) and consequent improvement in mental health outcomes. However, these effects exhibit substantial heterogeneity across gender, employment types, and rural–urban residency. Notably, male elderly benefitted more from employment due to cultural and structural factors. Properties of employment status passage vary in rural areas of China, where the effect of self-employment is not significant, however the effect of Wage-employment is significant. Retirees may re-enter a social structure through status passages of employment or social participation. The study indicates that employment does not crowd out the elderly social participation but rather promotes their social participation, which is only reflected in elderly women and elderly persons in cities. This underscores the multifaceted mental health benefits of employment beyond mere economic contribution. Conclusion It is suggested to implement a flexible delayed retirement policy based on individual wishes, which would result in greater social welfare. For rural areas, it is imperative to address deficiencies in public cultural services while tapping into local cultural resources, thereby enhancing older residents’ mental health and well-being.

Energy consumption is a critical indicator of a country’s or region’s economic growth quality, reflecting the efficiency of its energy use, and guiding its efforts in energy conservation, consumption reduction, and scientific development.

Energy security is a very broad concept and has been defined differently in different countries at different times. There are almost one hundred interpretations of energy security and its subsidiary concepts in the existing documents summarized in the study. In the 1970s and 1980s, energy security meant a way of stabilizing the supply of imported oil at a reasonable price against the background of the ban on oil exports and the manipulation of prices by oligopolies. Thus, at that time, energy security was tantamount to having a stable international source of fossil energy. Energy security tended to vary from country to country, was evaluated within a single country, and initially represented energy security only for oil. Later, the widespread use of natural gas and coal mining also brought the security of these two energy sources into the system of energy security. Eventually, electricity security was also included in the concept of energy security.

Energy plays a critical role in human progress and is a key factor influencing climate change and environmental issues. Understanding global energy consumption trends in major economies such as the United States, China, and the European Union, Japan, India, Russia, and other countries is essential for managing energy supply and demand, formulating energy policies, and addressing global challenges.

From the perspective of coal consumption, China, as the world’s largest coal-consuming country, has maintained high levels of coal use because coal serves as the foundation of the country’s energy security. This reliance is inherently tied to China’s natural energy endowment.

Coal, often referred to as the “black gold” of the global energy system, has fueled industrial civilization but also imposed a significant environmental burden. Since the Industrial Revolution, coal has been a primary energy source due to its abundant reserves and high energy density. Even today, despite the rapid growth of renewable energy, coal continues to play an essential role in the global energy mix.

Since the construction of socialism with Chinese characteristics entered a new era, China’s requirements for high-quality development and ecological progress have gradually become higher and higher. China’s economy is shifting from a stage of rapid growth to a stage of high-quality development.

In recent years, China has also issued a series of policy documents related to reducing carbon emissions, such as the Guiding opinions of the State Council on accelerating the establishment and improvement of a green and low-carbon circular development economic system and the Interim Regulations on the Administration of Carbon Emission Trading, which proposed a series of measures and policies to promote China’s economy to develop in a green and low-carbon direction and reduce carbon emissions.

In recent years, there has been a notable increase interest in engineered living materials (ELMs) owing to their considerable potential. One key area of research within this field is the utilisation of various species of bacteria to create innovative living materials. In order to accelerate the advancement of bacterial‐based living materials, a systematic summary of bacterial species and their design strategies is essential. Yet, up to this point, no applicable reviews have been documented. This review offers a concise introduction to living materials derived from bacteria, delves into the strategies and applications of each bacterial species in this realm, and provides perspectives and future outlooks in this field.

Over the last decades, hospitals have faced shortages of medical personnel due to increasing demand. As one of the busiest divisions, the outpatient department plays a vital role in delivering public healthcare services, leading to a significant focus on physician work schedules. In this study, we develop a data-driven optimization framework for a mid-term period spanning several weeks within the outpatient department of a dermatology hospital. This framework integrates patient visit clustering and physician work scheduling sequentially, thereby ensuring its scalability for application in many other hospitals. We first employ a hybrid clustering model that classifies patient visits based on a joint distribution of physician-patient characteristics. This clustering model inherently captures patient preferences for physicians so that patient demand is stratified to each physician. Then, we propose a robust physician scheduling model based on a novel risk measure called Likelihood Robust Value at Risk (LRCVaR). In particular, the proposed LRCVaR considers the worst-case demand in an ambiguity set of possible distributions, leading to mitigated tail risks of service capacity shortages. Therefore, this scheduling model mitigates tail risks of service capacity shortages. A tractable reformulation of the proposed robust physician scheduling model is newly derived, and we show their equivalence using strong duality theory. An iterated algorithm for the reformulation is also delicately designed, and we demonstrate its applicability to off-the-shelf solvers. The case study demonstrates that our LRCVaR is less conservative while controlling for the risk level. Such a result indicates that our proposed approach can satisfy patient demand with a smaller number of physicians at the same level of risk. Dermatologists and venereologists serving as chief physicians in our studied hospital are more prone to reaching their service capacity limits. Thus, our framework outperforms existing robust approaches in reducing tail risks of capacity shortages and identifying the bottleneck of service provision.

In the face of land degradation and environmental constraints, it is imperative to have an adaptive financial geography structure and a land resource utilization system that supports corporate innovation. This study constructs a refined financial geographic accessibility measurement index. By integrating multi‐source spatio‐temporal big data, the study breaks through the static limitation of traditional statistical data. It accurately analyzes the spatial synergistic effect between the spatial distribution of financial institutions and land use planning. Land use data, such as spatial development rate and spatial interest points, provide high‐precision spatial evidence for revealing the mechanism of financial geographic accessibility affecting corporate innovation. Further, from the environmental sustainability perspective, this paper studies the moderating effect of environmental constraints on corporate innovation. Financial geographic accessibility can improve corporate innovation by reducing financing costs, accelerating knowledge spillover, realizing intermediate input sharing, improving labor matching, and giving play to location advantages. Notably, this facilitation effect performs better in cities with high energy consumption and carbon emissions. Heterogeneity analysis shows that proximity to the city center, low industrial maturity, government subsidies, soes, and large‐scale corporations significantly amplify the innovation benefits of financial geographic accessibility. This study combines remote sensing data with spatial big data to provide a new methodological framework for analyzing land use and degradation.

We consider a parabolic equation with a singular potential in a bounded domain Ω⊂Rn. The main result is a Lipschitz stability estimate for an inverse source problem of determining a spatial varying factor f(x) of the source term R(x,t)f(x). We obtain a consistent stability result for any μ≤p1μ*, where p1>0 is the lower bound of p(x) and μ*=(n−2)2/4, and this condition for μ is also almost a consistently optimal condition for the existence of solutions. The main method we used is the Carleman estimate, and the proof for the inverse source problem relies on the Bukhgeim–Klibanov method.