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The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.

Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.

This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.

This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

The main aim of this paper is to establish a reasonable and scientific evaluation index system to assess the high quality and full employment (HQaFE).

This paper uses a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria framework to evaluate the quality and quantity of employment, wherein the integrated weights of attributes are determined by the combined the Criteria Importance Through Inter-criteria Correlation (CRITIC) and entropy approaches.

Firstly, the gap in the Yangtze River Delta in employment quality is narrowing year by year; secondly, employment skills as well as employment supply and demand are the primary indicators that determine the HQaFE; finally, the evaluation scores are clearly hierarchical, in the order of Shanghai, Jiangsu, Zhejiang and Anhui.

A scientific and reasonable evaluation index system is constructed. A novel CRITIC-entropy-TOPSIS evaluation is proposed to make the results more objective. Some policy recommendations that can promote the achievement of HQaFE are proposed.

This study aims to delve into the varying impacts of different types of emotions conveyed through retailers' review request texts on consumers' intention to write a review.

To verify the relationships between these variables, two laboratory experiments were conducted in this study.

The findings indicate that when accompanied by an objective statement, texts that evoke empathy and favor have a positive influence on consumers' inclination to write a review. Moreover, by examining the underlying mechanism, this study uncovers two interconnected mediators, namely persuasive intent and cognitive (affective) resistance, along with empathy and helping intention. Additionally, the study explores the moderating role of customer satisfaction with the product, shedding light on the contextual factors that influence the effects of emotional cues in review texts.

This research contributes to the literature and practice by focusing on the process of retailers' generating online reviews. This is one of the first studies to systematically examine the effects of emotional text in retailers' review request on consumers' reviewing intention from the perspective of emotional evocation. The experimental findings and the underlying mechanisms emphasize the impact of different types of emotions in retailers' review requests texts on consumers' reviewing intentions. It can help retailers better understand the psychological reactions of consumers when they ask reviews, which provide theoretical support for retailers to design more reasonable asking texts.

In the supply chain disruption risk, the issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

Considering the influence of irrational emotions of decision makers, an evaluation model is designed by the regret theory and VIKOR method, which makes the decision-making process closer to reality.

The paper has some innovations in the evaluation index system and evaluation model construction. The method has good stability under the risk of supply chain interruption.

The mixed evaluation information is used to describe the attributes, and the evaluation index system is constructed by the combined method of the social network analysis method and the literature research method to ensure the accuracy and accuracy of the extracted attributes. The issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

The study aims to draw on the self-congruity theory to investigate the relationship among destination personality (DP), self-congruity and tourists’ pro-environmental behavioral intention (BI) among Guangzhou citizens who have experienced nature-based tourism (NBT).

The survey-based quantitative research was divided into two rounds, namely, a preliminary study exploring the dimensions of DP and the verification of whether the DP dimensions that significantly affect pro-environmental BI in step one would be selected for the main research to validate the conceptual model.

The results suggest that wholesome, one of the destination personalities, strongly predicts tourists’ pro-environmental BI, while actual self-congruity plays a mediating role between sincere, another DP, and tourists’ pro-environmental BI.

In practice, it offers multidimensional knowledge and robust evidence-based recommendations for the sustainable development and destination branding of NBT destinations in the post-epidemic era.

The study presents pioneering work that reveals previously underestimated factors influencing pro-environmental BI.