Xin Yang

This study investigates the influence of greenwashing on the alignment between firm-level stock returns and broader market trends, with particular attention to the mediating roles of investor sentiment, media coverage and sustainable fund ownership. The findings reveal that greenwashing significantly reduces return synchronicity by embedding unreliable firm-specific environmental signals into stock prices, thereby impairing market efficiency. The results demonstrate that greenwashing amplifies sentiment-driven trading among investors, attracts increased media coverage that further disseminates and legitimizes biased environmental narratives, and influences institutional investment decisions—particularly by sustainable funds, which tend to rely on superficial environmental disclosures. Additionally, the heterogeneous analysis indicates that the influence of greenwashing on stock price synchronicity is insignificant in heavily polluting industries due to heightened regulatory oversight and stakeholder scrutiny. This research adds to existing knowledge by linking greenwashing to stock price synchronicity, providing new insights into how selective environmental disclosures influence market behavior. By exposing the negative consequences of biased environmental disclosures, this study highlights the urgent need for stronger accountability standards, robust environmental reporting frameworks, and stricter regulatory oversight. Such measures are essential to ensure transparency, uphold market integrity, and protect investors from the adverse effects of deceptive corporate practices.

Knowledge of three-dimensional structures of each individual particles of asymmetric and flexible proteins is essential in understanding those proteins' functions; but their structures are difficult to determine. Electron tomography provides a tool for imaging a single and unique biological object from a series of tilted angles, but it is challenging to image a single protein for three-dimensional reconstruction due to the imperfect mechanical control capability of the specimen goniometer under both a medium to high magnification and an optimized beam coherence condition. Here, we report a fully mechanical control method for automating ET data acquisition without using beam tilt/shift processes. This method could reduce the accumulation of beam tilt/shift that used to compensate the error from the mechanical control, but downgraded the beam coherence. Our method was developed by minimizing the error of the target object center during the tilting process through a closed-loop proportional-integral control algorithm. The validations by both negative staining and cryo-electron microscopy suggest that this method has a comparable capability to other ET methods in tracking target proteins while maintaining optimized beam coherence conditions for imaging.

Internet of Things is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things is the expansion of the Internet of Health Things which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT.

BackgroundPsychological resilience may reduce the impact of psychological distress to some extent. We aimed to investigate the mental health status of the public during the outbreak of coronavirus disease 2019 and explore the level and related factors of anxiety and depression.MethodsFrom February 8 to March 9, 2020, 3,180 public completed the Zung’s Self-Rating Anxiety Scale for anxiety, Zung’s Self-Rating Depression Scale for depression, the Connor–Davidson resilience scale for psychological resilience, and the Simplified Coping Style Questionnaire for the attitudes and coping styles.ResultsThe number of people with depressive symptoms was 1,303. The number of people with anxiety symptoms was 1,184. The depressed group and anxiety group had less education, more unmarried and younger age, as well as had significant different in SDS total score, SAS total score, CD-RISC total score, and SCSQ score. The binary logistic regression showed that female, strength, and the subscales of active coping style in SCSQ remained protective factors and passive coping style and higher SAS score were risk factors for depression. Optimism in CD-RISC was a protective factor, and passive coping styles and higher SDS score were risk factors for anxiety.LimitationsThis study adopted a cross-sectional design and used self-report questionnaires.ConclusionThe mental health of the public, especially females, the younger and less educational populations, and unmarried individuals, should be given more attention. Individuals with high level of mental resilience and active coping styles would have lower levels of anxiety and depression during the outbreak of COVID-19.