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Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

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.

This paper aims to reveal the influence of the grooved texture parameters on the lubrication performance of circular pocket-roller pairs in cylindrical roller bearings.

In this paper, the thermal elastohydrodynamic lubrication mathematical model of the grooved texture circular pocket-roller pair was established, the finite difference method and successive over-relaxation method were used to solve the model, the influence of texture quantity, texture depth and texture area ratio on circumferential bearing capacity, friction coefficient, maximum temperature rise, stiffness and damping of the circular pocket-roller pairs were analyzed.

The results show that texture quantity, texture depth and texture area ratio significantly influence the static and dynamic characteristics of circular pocket-roller pairs. The suitable surface groove texture parameters can dramatically improve the circumferential bearing capacity, reduce the friction coefficient, inhibit the maximum temperature rise and increase the stiffness and damping of the circular pocket-roller pairs.

The research in this paper can provide a theoretical basis for the optimization design of pockets in cylindrical roller bearings to reduce friction and vibration.

This paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.

This paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.

Experimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.

This paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.

Against the background of the popularity of social media and heritage tourism, this study aims to focus on world heritage sites, proposing a method to examine and compare the digital spatial footprints left by tourists using geographic information systems.

By analyzing user-generated content from social media, this research explores how digital data shapes the destination image of WHS and the spatial relationships between the components of this destination image. Drawing on the cognitive-affective model (CAM), it investigates through an analysis of integrated data with more than 20,000 reviews and 2,000 photos.

The creativity of this research lies in the creation of a comprehensive method that combines text and image analytics with machine learning and GIS to examine spatial relationships within the CAM framework in a visual manner.

The results reveal tourists' perceptions, emotions, and attitudes towards George Town and Malacca in Malaysia, highlighting several key cognitive impressions, such as history, museums, churches, sea, and food, as well as the primary emotions expressed. Their distributions and relationships are also illustrated on maps.

Tourism practitioners, government officials, and residents can gain valuable insights from this study. The proposed methodology provides a valuable reference for future tourism studies and help to achieve a sustainable competitive advantage for other heritage destinations.

Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.

The authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.

Results showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.

The research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.

The INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.

This study aims to understand the influence of raceway surface topography on the temperature rise characteristics of silicon nitride (Si₃N₄) full ceramic ball bearing and improve its service life.

The arithmetic average height S_a, skewness S_sk and kurtosis S_ku in the three-dimensional surface roughness parameters are used to quantitatively characterize the surface topography of the raceway after superfinishing. The bearing life testing machine is used to test the Si₃N₄ full ceramic ball bearing using polytetrafluoroethylene (PTFE) cage under dry friction conditions, and the self-lubricating full ceramic ball bearing heat generation model is established.

With the decrease of S_a and S_sk on the raceway surface and the increase of S_ku, the average height of the raceway surface decreases, and the peaks and valleys tend to be symmetrically distributed on the average surface, and the surface texture becomes tighter. This kind of raceway surface topography is beneficial to form a thin and uniform filamentous PTFE transfer film with a wide coverage area on the raceway surface based on consuming less cage materials and improving the temperature rise characteristics of hot isostatic pressing silicon nitride full ceramic ball bearings.

The research results provide a theoretical basis for the reasonable selection of Si₃N₄ ring raceway processing technology and have important significance for improving the working characteristics and service life of Si₃N₄ full ceramic ball bearings under dry friction conditions.

This study dwells into the crucial aspects of gig workers' jobs that are absent, with specific focus on the work life of food delivery workers (FDWs) and how it impacts their work happiness.

To create a conceptual model, 21 delivery workers were first interviewed, and the data gathered were scrutinized. Subsequently, a questionnaire was sent to 493 delivery partners, probing about their opinions of work factors that could affect their level of happiness. The collected data were put to study by the authors using AMOS and SPSS.

Five missing work components were revealed by qualitative investigation. The absence of voice, recognition, career growth, work satisfaction, and dignity at work contributed to unhappiness of the workers. The qualitative analysis was supported by quantitative findings. Additionally, company policy moderated the relationship between absence of voice, absence of career growth, absence of job satisfaction and absence of work happiness.

To the best of the authors’ knowledge, this is one of the first studies using a mixed-method approach to identify factors contributing to the unhappiness of FDWs in work. The originality of this study also lies in establishing the moderating influence of company policy on the relationship between the absence of voice, absence of career growth, absence of work satisfaction and absence of workplace happiness among the workers.

Therapeutic landscapes can be beneficial for patients recovering from cognitive and behavioural impairments. The therapeutic garden in the Royal Talbot rehabilitation centre (RTRC) located in Kew, Melbourne is a precedent for its nature design outdoor spaces and horticultural therapy (HT) program for patients with cognitive impairments due to brain and spinal cord injuries. There have been no studies into the physical design of the RTRC therapeutic garden.

This case study unveils the critical design themes, and dominant physical elements of the RTRC therapeutic garden through site observation and analysis over the course of two months, a semi-structured interview with a key staff, and graphical representations using integrated photography and collaborative digital tools of AutoCAD and Adobe Photoshop.

Seven critical design themes are identified for the RTRC garden: (1) space layout and organization, (2) physical and visual access, (3) pathway and facility, (4) multisensory vegetation, (5) amenity, wayfinding and seasonal interest, (6) cognitive stimulation, (7) utility and HT. Qualitative and quantitative data analysed in visual and descriptive format reveal multisensory vegetation, amenity, wayfinding and seasonal interest, and utility and HT are the top three most pivotal critical design themes in the RTRC therapeutic garden.

The findings of this case study can be used to impart knowledge about the design of therapeutic landscape settings for cognitive impairments to design professionals and the public.

There is no case study has been conducted for this precedent of therapeutic garden in the Royal Talbot rehabilitation centre that targets for cognitive impairment patients. With the increasing awareness in medical and healthcare environments, this case study would help to inject mutual understanding, generate knowledge and design awareness among design professionals and public, and to cultivate more good quality healthcare settings.

The main objective of this study is to determine the factors that have the greatest impact on travelers' opinions of airports.

11,656 customer reviews for 649 airports around the world were gathered following the COVID-19 outbreak from the website that rates airport quality. The dataset was examined using hierarchical regression, PLS-SEM, and the unsupervised Bayesian algorithm-based PSEM in order to verify the hypothesis.

The results showed that people’s intentions to recommend airports are significantly influenced by their opinions of how well the servicescape, staff, and services are.

By encouraging air travelers to have positive intentions toward recommending the airports, this research offers airport managers decision-support implications for how to improve airport service quality. This will increase the likelihood of retaining more passengers.

This study also suggests a quick-to-implement visual decision-making mechanism based on PSEM that is simple to understand.