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With the rise of esports, research on the perceived fit between esports sponsors and events remains limited. This study uses the Elaboration Likelihood Model (ELM) to investigate how the perceived fit between sponsors and esports events effects brand awareness, consumer attitudes and purchasing behavior, aiming to offer insights for more effective marketing strategies.

Data were collected from 245 participants during the Taipei Game Show 2024, using purposive sampling of individuals aged 18 and above. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0.1.6 to examine the relationships among perceived fit, brand awareness, consumer attitudes and purchasing behavior, and to investigate the mediating effects.

The results discovered that brand awareness, perceived fit and consumer attitudes had significant positive effects on purchasing behavior, explaining 75% of its variance. Additionally, perceived fit positively affected both brand awareness and consumer attitude. Mediating effect showed that both brand awareness and consumer attitude play significant mediating roles between perceived fit and purchasing behavior, with consumer attitude having a stronger mediating effect.

This study highlights to the limited body of research on esports sponsorships by demonstrating that perceived sponsor-event fit is crucial for enhancing brand awareness, advancing positive consumer attitudes and driving purchasing behavior. The ELM framework highlights the importance of central and peripheral routes in influencing consumer decisions, offering strategies for companies to optimize sponsorship effectiveness and improve brand competitiveness.

This study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.

The research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.

The analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.

This study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

The purpose of this study is to analyze the dynamic changes of the arms trade network not only from the network structure but also the influence mechanism from the aspects of the economy, politics, security, strategy and transaction costs.

The study employs the Temporal Exponential Random Graph Model and the Separable Temporal Exponential Random Graph Model to analyze the endogenous network structure effect, the attribute effect and the exogenous network effect of 47 major arms trading countries from 2015 to 2020.

The results show that the international arms trade market is unevenly distributed, and there are great differences in military technology. There is a fixed hierarchical structure in the arms trade, but the rise of emerging countries is expected to break this situation. In international arms trade relations, economic forces dominate, followed by political, security and strategic factors.

Economic and political factors play an important role in the arms trade. Therefore, countries should strive to improve their economic strength and military technology. Also, countries should increase political mutual trust and gain a foothold in the industrial chain of arms production to enhance their military power.

The contribution of this paper is to analyze the special trade area of arms trade from a dynamic network perspective by incorporating economic, political, security, strategic and transaction cost factors together into the TERGM and STERGM models.

This paper attempts to combine the application of artificial intelligence in predicting and evaluating the classification of surrounding rock grades and provides guidance for subsequent support design and reinforcement support operations.

This paper discusses the use of BPNN as the primary tool, combined with three swarm bionic optimization algorithms (GA, PSO, GWO), to solve stability evaluation and grade prediction of surrounding rock in ultra-deep roadway excavation.

Taking the Great Wall ore group as the core and the Shanghaimiao mining area as the extension, the optimal model is applied to the classification of surrounding rock grade in ultra-deep roadway engineering. Prediction results show that the performance of BPNN models is excellent.

Due to the limitations of geological conditions and construction environment in deep coal mines, the period of roadway excavation is too long, resulting in less data collection.

The prediction results can provide guidance for the excavation method, support scheme correction and reinforcement support scheme design of deep coal mine roadway engineering.

It provides guidance for deep mining of coal mine (the premise of surrounding rock support stability), so as to ensure the economic and safety benefits of coal enterprises.

The neural network is applied to rock mechanics in a deep site for the first time, which is used to solve the prediction direction of surrounding rock grade evaluation. The index of the input layer is determined by combining the “three high and one disturbance” with the on-site construction situation, which is closer to the actual project. The swarm intelligent bionic algorithms are selected to optimize the hyperparameters of back propagation neural network, so as to improve the accuracy of the models. The classification and evaluation system of surrounding rock for the Great Wall ore group is constructed, which is the core of Shanghaimiao mining area in the northwest of China, guiding the dynamic adjustment of on-site excavation and support operations.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

This study examines the temporal dynamics of consumer attitudes, perceived value and purchase intentions toward green agricultural foods, addressing critical gaps in the literature on sustainable consumption behaviours. It emphasises the mediating role of perceived value and its evolution over time, offering insights into consumer decision-making processes.

A longitudinal design was adopted, collecting data through structured questionnaires from primary household food purchasers in northern Taiwan at baseline, three months and six months. Analytical techniques, including multiple regression, mediation analysis and repeated measures ANOVA, were employed to examine relationships and track changes over time.

The results reveal that consumer attitudes positively influence perceived value, which fully mediates the relationship with purchase intentions. Temporal analysis indicates significant increases in perceived value and purchase intentions over six months, demonstrating that sustained exposure to green agricultural foods reinforces consumer commitment and pro-environmental behaviours. Attitudes alone do not directly predict purchase intentions without the mediation of perceived value, highlighting the critical role of perceived benefits in driving long-term sustainable consumption.

This study provides actionable insights for enhancing the perceived value of green agricultural foods. Businesses should prioritise health and environmental benefits, while policymakers can design campaigns and incentives to promote sustainable dietary habits, aligning with Sustainable Development Goal 12.

By exploring the mediating role of perceived value in transforming positive consumer attitudes into purchase intentions, this study highlights how perceived value, shaped by health and environmental benefits, drives consumer behaviour. These findings contribute valuable insights for enhancing market appeal and supporting sustainable food marketing strategies.

To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

This study aims to focus on optimization of process parameters for porosity and strength of polyamide porous bone scaffolds fabricated via selective laser sintering (SLS) process.

Taguchi’s design of experiment approach with L18 orthogonal array (OA) has been used to optimize the process parameters. Five process and four response parameters have been considered for this study. Initially, minimum size of the pores that can be depowdered was identified. Then, porous CAD models of test specimen to measure porosity and strength were designed in Solidworks® software and fabricated using EOSINT P395 m/c. Signal-to-noise ratio and analysis of variance were used to identify the optimal levels of parameters and statistical significance of the parameters.

Among five parameters, powder refresh rate, build chamber temperature and layer thickness were found to have significant influence on all the response parameters, whereas build orientation and build position were found insignificant for all the responses. The Taguchi’s confirmation test validated the results of the study with maximum deviation of 5.8% for compressive strength. Comparison of predicted and experimental values revealed a satisfactory predictability of all the developed linear regression models.

This study reveals optimal set of parameters for SLS of the polyamide porous bone scaffolds. The optimal set of parameters may be used by other researchers to get enhanced combination of strength and porosity while fabricating porous scaffolds.

The conventional customer lifecycle fails to acknowledge the “sleeping” stage between regular patronage and churn, particularly prevalent in the hospitality industry. This study constructs an awakening model to regain “sleeping” guests.

342 questionnaires from Macau using partial least squares-structural equation modeling (PLS-SEM) were analyzed. The model was compared across different membership levels through multigroup analysis.

The results indicate that the point policy can awaken “sleeping” guests by influencing their perceived value, regret, and integrated satisfaction with a shorter “sleeping” period. Two path coefficients showed significant differences among basic and elite members.

Companies with loyalty programs should implement a transitional period before resetting points, leveraging altruistic point policies to awaken “sleeping” guests via direct communication. This strategy mitigates the negative impact of finite point expiration policies, enhancing customer re-engagement and point utilization.

Our study focuses on a crucial facet of hotel marketing—customer regain strategies. By identifying customer segments who have not revisited the hotel group for more than twelve months, we confirm the concept of “sleeping” guests. This term offers a nuanced perspective, distinguishing “sleeping” guests from generic lost customers. The “sleeping” guest segment provides valuable insights for enhancing targeted and effective marketing activities in the highly competitive hotel industry.