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The precast concrete slab track (PST) has advantages of fewer maintenance frequencies, better smooth rides and structural stability, which has been widely applied in urban rail transit. Precise positioning of precast concrete slab (PCS) is vital for keeping the initial track regularity. However, the cast-in-place process of the self-compacting concrete (SCC) filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC, even cracking of PCS. Currently, the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.

In this work, a Computational Fluid Dynamics (CFD) model is established to calculate the buoyancy of PCS caused by the flowing SCC. The main influencing factors, including the inlet speed and flowability of SCC, have been analyzed and discussed. A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.

The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.

The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.

The landscape of health information acquisition has shifted from offline to online, and online question-and-answer (Q&A) communities have emerged as prominent sources of health information; however, it is unclear how users identify satisfactory health information. This paper identifies factors that influence users’ adoption of health information in the context of online Q&A communities.

Based on the elaboration likelihood model (ELM) and opinion leader theory, we construct a research model to examine how information quality (complexity, image structure and emotional change) and source credibility (authentication status, follower number) affect health information adoption behavior. We verify the hypotheses by Poisson regression and zero-inflation Poisson regression using the data collected from an online Q&A community.

The empirical results indicate that both information quality and source credibility positively affect users’ adoption of health information.

This research can assist designers and managers of online Q&A communities to better comprehend users’ health information needs and their preferences for adoption. This enhanced understanding can facilitate the provision of superior online health information.

With the escalation of economic and environmental concerns, there is growing interest in electric automobiles. Increased interest has led to the need for electric car-charging stations. The strategic placement of an appropriate number of electric vehicle charging stations is crucial for sustainability. A literature search was first undertaken to establish the criterion. This study aims to determine the number and variety of charging stations in several alternative districts according to the defined criteria.

Geographic Information System is utilized to collect data on the specific criteria of the selected research region. MACBETH was used to establish criterion weights. A mixed-integer mathematical model was developed to determine the optimal number of charging stations in a specified location based on the acquired data and criterion weights while adhering to predefined limits.

The results provided an integrated method for determining a sufficient number of charging stations by considering the chosen criteria and restrictions. This study seeks to enhance the existing literature on decision-making frameworks for determining the number of charging stations by utilizing an integrated Geographic Information System based on MACBETH, together with mixed-integer programming.

This study integrates qualitative and quantitative data to enhance managerial and practical implications. The application of MCDM and mathematical modeling presents managerial implications that affect growth, operational efficiency and sustainability objectives. Regarding practical implications, the proposed method helps managers evaluate potential locations based on factors, such as cost, geography, resource proximity, traffic patterns and power grid capacity.

Currently, the majority of cars powered by petroleum oil and its by-products have a substantial adverse effect on sustainability due to heightened emissions of hydrocarbons, contributing to global warming and noise pollution. In addition, with the rise in gasoline costs, alternative energy sources are being explored.

The purpose of this study is to explore the impact of perceived experience with ChatGPT on online consumers' information searching behavior. The study also examines the moderating effects of those relationships.

Primary data were collected through an online survey. In total, 370 eligible responses were received. This study applied partial least squares structural equation modeling (PLS-SEM) for data analysis.

The findings demonstrate that both perceived personalization and perceived relevance have a direct impact on online consumers' information searching behaviors. Additionally, the results also indicate that perceived accuracy and perceived convenience lead to positive online consumers' information searching behavior. Moreover, age and gender and education level play moderating mechanisms in this relationship.

Overall, the conclusions of this study provide valuable insights into the potential of ChatGPT to improve online consumers behavior. However, there are still many unanswered questions about the impact of ChatGPT on consumers' experience. Future research is needed to explore these questions and to further our understanding of the potential of ChatGPT to revolutionize online information searching and digital marketing.

To the best of the authors’ knowledge, this paper is the first of its kind to highlight the impact of perceived experience with ChatGPT on online consumers' information searching behavior of its extensive use in scientific research and academic work. The importance of this study lies in the fact that it presents the behaviors concerns and future fears of people in academia as they cope with and deal with the inevitable reality of artificial intelligence (AI) language models such as ChatGPT.

Traditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.

We propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.

The proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.

The adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.

This study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

The theme park industry has attracted wide attention and visitors’ perceptions are key to improving theme park management. Applying the cognitive-affective theory, this study aims to explore various cognitive attributes and affective attitudes and how they affect the overall theme park image.

A mixed research method was used to analyze tourists’ cognitive, affective and overall evaluations of theme parks through text mining and logistic regression and to verify their internal relationships.

Study 1 confirms the impact of six features of theme parks, including food and beverage consumption, merchandising, spatiality, immersive technologies, interactive performances and thematization. Study 2 reveals that finer-grained emotions such as goodness, sadness, disgust, surprise, fear, joy and anger are present in visitor reviews. Study 3 confirms the significant influence of cognitive characteristics and emotions related to theme parks on the overall image through regression analysis. The findings carry meaningful implications for theme park managers, offering guidance on customer needs, perceived negative attributes and how to improve visitor experiences.

This study explores the attribute characteristics of cognitive and affective images of theme parks and their influence on the overall image, thereby enriching the research on the connotations of cognitive-affective theory. In particular, this study introduces and quantitatively analyses the concept of theme parks for the first time through a large-scale data analysis, which empirically reconciles the contradictions of previous reviews of different definitions of theme parks.

主题公园评论：文本挖掘的认知特征和情感如何决定主题公园形象

摘要

主题公园行业备受关注, 而游客的看法是改进主题公园管理的关键所在。本研究运用认知-情感理论, 旨在探索游客评论的认知属性与情感态度, 以及它们如何影响主题公园的整体形象。

采用混合研究方法, 通过文本挖掘和逻辑回归分析游客对主题公园的认知、情感及总体评价, 并验证它们之间的内在关系。

研究1证实了主题公园的六个特征所产生的影响, 包括餐饮消费、商品销售、空间性、沉浸式技术、互动表演和主题化。研究2表明, 游客评价中存在更细化的情感, 如好感、悲伤、厌恶、惊讶、恐惧、喜悦和愤怒。研究3通过回归分析证实了与主题公园相关的认知和情感特征对整体形象具有显著影响。这些研究结果对主题公园管理者具有重要意义, 为了解客户需求、识别负面属性以及如何改善游客体验提供了指导。

本研究对主题公园认知和情感形象的属性特征及其对整体形象的影响进行了探索, 从而丰富了认知-情感理论内涵。特别是本研究首次通过引入大规模数据并量化分析了主题公园的概念, 从实证角度调和了以往针对主题公园不同定义的相关评论中存在的矛盾。

Reseñas de parques temáticos: cómo la minería de textos de las características cognitivas y las emociones pueden determinar la imagen del parque temático

Resumen

El sector de los parques temáticos ha atraído una gran atención y las percepciones de los visitantes son clave para mejorar su gestión. Aplicando la teoría cognitivo-afectiva, este estudio pretende explorar diversos atributos cognitivos y actitudes afectivas, y cómo afectan a la imagen global del parque temático.

Se adoptó un enfoque de investigación de métodos mixtos para analizar las evaluaciones cognitivas, afectivas y globales de los turistas sobre los parques temáticos mediante minería de textos y regresión logística, y para validar la relación intrínseca entre ellas.

El estudio 1 confirmó el impacto de seis características de los parques temáticos, como el consumo de alimentos y bebidas, el merchandising, la espacialidad, las tecnologías inmersivas, los espectáculos interactivos y la tematización. El estudio 2 reveló la presencia de emociones más sutiles en las evaluaciones de los visitantes, como la bondad, la tristeza, el asco, la sorpresa, el miedo, la alegría y la ira. El estudio 3 confirmó, mediante un análisis de regresión, que las características cognitivas y las emociones asociadas a los parques temáticos tienen un efecto significativo en la imagen global. Estas conclusiones tienen importantes implicaciones para los gestores de los parques temáticos, ya que proporcionan orientación para comprender las necesidades de los clientes, identificar los atributos negativos percibidos y saber cómo mejorar las experiencias de los visitantes.

Este estudio explora las características de los atributos de las imágenes cognitivas y afectivas de los parques temáticos y su impacto en la imagen global, enriqueciendo así la investigación sobre las connotaciones de la teoría cognitivo-afectiva. En particular, este estudio introduce y analiza cuantitativamente por primera vez el concepto de parque temático mediante un análisis de datos a gran escala, que concilia empíricamente las contradicciones que existían en revisiones anteriores de las distintas definiciones de parque temático.

Gamification has been widely applied in mobile fitness apps to motivate users to exercise continuously. Based on the affordances–psychological outcomes–behavioral outcomes framework, this study explores the roles of three specific gamification affordances (competition, visibility of achievement and interactivity) in self-health management (continuous use behavior and health behavior) from the perspectives of achievement satisfaction and gamification exhaustion.

We test the research model using a structural equation model (SEM) with 505 self-reported data points. Furthermore, we apply fuzzy-set qualitative comparative analysis (fsQCA) to explore configurations of gamification affordances associated with self-health management behavior, reinforcing the SEM results.

Results indicate that competition, visibility of achievement and interactivity can enhance achievement satisfaction, which further boosts self-health management behavior. However, competition and interactivity can also cause gamification exhaustion, which undermines self-health management behavior to some extent. Overall, the positive impacts of the three affordances outweigh the negative impacts.

This study provides new insights for relevant practitioners on designing gamification affordances, aiding the sustainable development of mobile fitness apps and their long-term effects on self-health management. Visibility of achievement should be emphasized, and competition and interactivity should be thoughtfully designed to minimize their negative effects.

This study extends the affordances–psychological outcomes–behavioral outcomes framework and the literature on gamification and health management by applying both SEM and fsQCA methodologies to examine the relationship between specific gamification affordances and self-health management behavior.

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.

Silicon-containing groups were introduced into fluoroacrylate polymer to further improve the comprehensive performance of pressure-sensitive adhesive (PSA) for expanded polytetrafluoroethylene (ePTFE) bonding.

A series of silicon-containing fluorinated acrylic copolymers were synthesized through free radical solution polymerization with vinyloxy trimethylsilane, allyltrimethylsilane, 3-(trimethoxysilyl)propyl methacrylate or 1,3,5-tris(3,3,3-trifluoropropyl) methylcyclotrisiloxane as silicon monomers, and comprehensive performance of the copolymers was evaluated based on Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), gel permeation chromatography, glass transition temperatures (T_g), differential scanning calorimetry, thermogravimetric analysis, water contact angle, the track, 180° peel strength, and shear holding power.

Based on the FTIR and XPS results, it is confirmed that the silicon monomers were successfully introduced into the fluorinated acrylate copolymer. XPS analysis indicated that the silicon groups had the tendency to enrich on the surface of the film, thereby reducing the F content on the film surface. The glass transition temperatures (T_g) of the PSAs increased when silicon monomers were introduced, while the thermal stability declined. The contact angles of the acrylic PSA films were increased with the introduction of silicon monomers. From the perspective of bonding performance, the track, 180° peel strength and shear holding power decreased to varying degrees compared to silicon-free PSA, except significantly elevated holding power with MPS as the silicon monomer.

Silicon-containing fluorinated acrylic copolymers were synthesized, and the comprehensive performance was evaluated as PSAs of ePTFE for the first time.

As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.