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[Objective] Whether “digitization” and “green” can cooperate is one of the important scientific issues in the field of high-quality development and high-level protection research. The Yangtze River Economic Belt, as a significant cluster of the digital industry and a key area for pollution reduction and carbon emission reduction coordination, plays a pivotal role in the construction of a beautiful China and the realization of the dual carbon goals. [Methods] Based on the equity investment data of the digital industry and the environmental pollution and carbon emission data from 108 cities in the Yangtze River Economic Belt between 2010 and 2020, this study delineated the spatial pattern of the digital industry and the spatial and temporal change characteristics of pollution reduction and carbon emission reduction. A fixed-effect model was employed to explore the impact of the digital industry agglomeration on pollution reduction and carbon emission reduction coordination and mechanism. Additionally, a spatial Durbin model was constructed to analyze its spatial spillover effects and attenuation boundaries. [Results] The findings reveal that: (1) The investment network of the digital industry in the Yangtze River Economic Belt exhibits a “multi-pathway, multi-polar” trend of spatial change, with net investment flows showing a clear tendency towards suburbanization, forming a “outflow from the center, receiving by the periphery” distribution pattern. (2) The agglomeration of the digital industry positively drives pollution reduction and carbon emission reduction, also demonstrating synergistic effects for both. This result remains robust after a series of endogeneity and robustness tests. Mechanism tests found that digital industry agglomeration can promote regional pollution reduction and carbon emission reduction coordination through scale economy effects, industrial upgrading effects, and technological innovation effects. (3) Digital industry agglomeration has a significant positive spatial spillover effect on pollution reduction and carbon emission reduction coordination. This effect presents a “right-tailed U-shaped” characteristic with increasing spatial distance. Meanwhile, the area within 250 km of the agglomeration is the “enhancement area” for spatial spillover effects, the area between 250 km and 450 km is the “attenuation area”, and beyond 450 km, the effect approaches zero and becomes insignificant. [Conclusion] Research has confirmed that leveraging the agglomeration effects of the digital industry can effectively promote synergistic improvements in regional pollution reduction and carbon emission mitigation.It is recommended to foster the agglomeration and networking development of the digital industry, encourage cross-regional investment in the digital industry, and implement differentiated strategies and cross-regional cooperation platforms.

为给黄油中复配天然抗氧化剂的开发提供思路，通过DPPH法测定DPPH自由基清除率和Rancimat法测定氧化诱导时间，研究单一和复配添加α-生育酚、γ-谷维素和植物甾醇3种天然抗氧化剂对黄油氧化稳定性的影响。结果表明：3种天然抗氧化剂均能提高黄油的氧化稳定性；不同复配组合对黄油氧化稳定性的影响有差异，其中α-生育酚和γ-谷维素（125 mg/kg和1 250 mg/kg）、α-生育酚和植物甾醇（125 mg/kg和1 250 mg/kg）、γ-谷维素和植物甾醇（均为800 mg/kg）3种复配组合对黄油氧化稳定性的提升较大，特别是α-生育酚和γ-谷维素（125 mg/kg和1 250 mg/kg）组合的氧化诱导时间可提升3.70 h，DPPH自由基清除率达80.4%，氧化稳定性能优于其他复配组合。综上，将复配天然抗氧化剂添加到黄油中可提高其氧化稳定性。 To provide ideas for the development of compounded natural antioxidants in butter, DPPH radical scavenging rate was evaluated by DPPH method and oxidation induction time was analyzed by Rancimat method. The effects of single or compound addition of three natural antioxidants, α-tocopherol, γ-oryzanol and phytosterol on oxidative stability of butter were investigated. The results showed that all three natural antioxidants could enhance oxidative stability of butter, and different combinations had different effects on oxidative stability of butter. With the three combinations of α-tocopherol and γ-oryzanol (125 mg/kg and 1 250 mg/kg), α-tocopherol and phytosterol (125 mg/kg and 1 250 mg/kg), and γ-oryzanol and phytosterol (both 800 mg/kg) showed greater improvement in the oxidative stability of butter. In particular, the oxidation induction time of group α-tocopherol and γ-oryzanol (125 mg/kg and 1 250 mg/kg) could be increased by 3.70 h, and the DPPH radical scavenging rate reached 80.4%, and the oxidative stability was better than other combinations. In conclusion, the addition of the compounded natural antioxidants to butter can improve its oxidative stability.

Medical large language models are being introduced to the public in collaboration with governments, medical institutions, and artificial intelligence (AI) researchers. However, a crucial question remains: Will patients follow the medical advice provided by AI doctors? The lack of user research makes it difficult to provide definitive answers. Based on the clinician-patient communication pathway model, this study conducted a factorial experiment with a 2 (medical provider, AI vs. human) × 2 (information support, low vs. high) × 2 (response latency, slow vs. fast) between-subjects design (n = 535). The results showed that participants exhibited significantly lower adherence to AI doctors’ advice than to human doctors. In addition, the interaction effect suggested that, under the slow-response latency condition, subjects perceived greater health benefits and patient-centeredness from human doctors, while the opposite was observed for AI doctors. [ABSTRACT FROM AUTHOR]

AbstractThe commercialization of artificial intelligence (AI) in healthcare is accelerating, yet academic research on its users remains scarce. To what extent are they willing to disclose personal health privacy to AI doctors compared to traditional human doctors? What factors are shaping these decisions? The lack of user research has left these questions unanswered. This article, based on privacy calculus theory, conducted a multi-factorial between-subjects online experiment (N = 582) with a 2 (medical provider: AI vs. human) × 2 (emotional support: low vs. high) × 2 (information sensitivity: low vs. high) design. The results indicated that AI doctors lead participants to perceive both lower health benefits and privacy risks. Emotional support is not always beneficial. On one hand, high emotional support can provide patients with more health benefits, but on the other hand, it also poses higher levels of privacy risks. Additionally, high emotional support responses from AI doctors could enhance patients’ health benefits, trust, and willingness to disclose health privacy, while the opposite was observed for human doctors. [ABSTRACT FROM AUTHOR]

Air pollutants, primarily PM2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an "M"-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management. [ABSTRACT FROM AUTHOR]