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During the COVID-19 pandemic, the public relied heavily on digital media to stay tuned for the latest update. Media preference could increase risk perceptions, although the influence of diverse media exposure remains unknown. Based on protection motivation theory, this study aims to investigate how digital media exposure diversity and information verification influence vaccination intention.

Analyzing survey data from 837 respondents in China, this study examined the effects of digital media exposure on information verification, including their influences on the threat appraisal, coping appraisal, vaccine misinformation beliefs, subjective norms and trust in vaccines.

Results indicate that diversity of digital media exposure increased threat appraisal (perceived severity) and coping appraisal (response efficacy and self-efficacy), while information verification increased only coping appraisal (response efficacy and self-efficacy). In addition, diversity of digital media exposure decreased vaccine misinformation beliefs. Furthermore, digital media exposure and information verification were linked to vaccination intention via the mediations of response efficacy, subjective norms and trust in vaccines.

This study is the first of its kind to investigate media exposure diversity in the context of vaccination and health crises. Our findings extended the PMT framework by exploring proactive information-related behaviors as antecedents of mediation processes. In addition, we examined misinformation beliefs, social norms and trust as societal influences. Theoretical and practical implications are also discussed.

The purpose of this study is to examine the impact of green bonds on common prosperity in China. Green bonds have gained significant attention as a means to address financial challenges and promote environmental protection. This research aims to investigate the influence of green bonds on common prosperity by utilizing the system-generalized method of moments (SYS-GMM) and analyzing panel data from prefecture-level cities. The study also explores the theoretical mechanisms and heterogeneous relationships between green bonds and common prosperity, providing valuable guidance for advancing economic and social well-being in China.

This study employs a system-generalized method of moments (SYS-GMM) as the methodology to investigate the influence of green bonds on common prosperity in China. Panel data from prefecture-level cities for the period 2014 to 2020 are utilized for analysis. The SYS-GMM approach allows for the examination of dynamic relationships and control of endogeneity issues. By utilizing this methodology, the study aims to provide robust and reliable findings on the impact of green bonds on common prosperity, considering the specific context of China's ecological civilization development and financial challenges faced by energy-saving and environmental protection enterprises.

The findings of this research indicate several important outcomes. Firstly, common prosperity in China experienced substantial growth between 2014 and 2020. Secondly, green bonds have demonstrated a clear and positive impact on common prosperity. They contribute to the enhancement of common prosperity by driving industrial structure upgrading and fostering green technology innovation. Lastly, the study reveals that the positive influence of green bonds on common prosperity is particularly pronounced in the western region of China. These findings highlight the significance of green bonds in promoting sustainable economic development and societal well-being.

This study contributes to the existing literature by examining the impact of green bonds on common prosperity in China, utilizing the system-generalized method of moments (SYS-GMM) and panel data analysis. The research not only adds to the understanding of the relationship between green bonds and economic well-being but also provides insights into the theoretical mechanisms and heterogeneous relationships involved. The findings showcase the positive influence of green bonds on common prosperity, emphasizing their role in addressing financial challenges, promoting environmental protection, and driving sustainable development. The study's conclusions offer valuable guidance for policymakers, financial institutions, and stakeholders in advancing common prosperity in China.

The purpose of this study is to investigate the influence of varying concentrations of nano-SiO₂ particle doping on the structure and properties of the micro-arc oxidation (MAO) coating of 7075 aluminum alloy. This research aims to provide novel insights and methodologies for the surface treatment and protection of 7075 aluminum alloy.

The surface morphology of the MAO coating was characterized using scanning electron microscope. Energy spectrometer was used to characterize the elemental content and distribution on the surface and cross section of the MAO coating. The phase composition of the MAO coating was characterized using X-ray diffractometer. The corrosion resistance of the MAO coating was characterized using an electrochemical workstation.

The results showed that when the addition of nano-SiO₂ particles is 3 g/L, the corrosion resistance is optimal.

This study investigated the influence of different concentrations of nano-SiO₂ particles on the structure and properties of the MAO coating of 7075 aluminum alloy.

This chapter reviews the competition between full-service carrier (FSC) and low-cost carrier (LCC) in China. More importantly, we discuss the impacts of the COVID-19 pandemic on FSC–LCC competition. Specifically, the airlines' route choices and also the market contact between FSCs and LCCs in China are examined and discussed. Our review results suggest that, despite the rapid growth of the independent LCC Spring Airlines and the establishment of new subsidiary LCCs by FSCs, China's LCC sector still plays relatively minor roles compared with many fully deregulated markets. Subsidiary LCCs serve more as competitive tools for their parent FSCs, primarily deployed on their parent FSCs' routes to jointly compete against rival FSCs. This competition is primarily focused on niche regional markets rather than engaging in full-scale competition. Spring Airlines also strategically avoided direct head-to-head competition with FSCs before the pandemic by mainly connecting with the secondary cities. However, the pandemic has introduced significant changes, notably the network differentiation between FSCs and LCCs in mainland China. With the relaxation of government's regulations on airline route entries into hub airports during pandemic, Chinese LCCs have shifted their focus toward serving more dense routes, especially those connected to the top five cities. This shift has led to an intensified head-to-head competition between LCCs and FSCs following the outbreak of the pandemic. Such a process is likely to continue in the years to come. This chapter's discussions could also provide new insights into LCC development and the impact of the pandemic on FSC–LCC competition interactions to supplement existing literature studying other major airline markets.

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).