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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.

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 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 assessment of risk to existing tunnels within the context of pit construction is influenced by a multitude of factors. The conventional fuzzy analytic hierarchy process (FAHP) method may lack precision due to its inability to incorporate the inherent randomness associated with numerous risk factors. To enhance the precision of risk evaluation for existing tunnels, this research introduces an improved FAHP approach grounded in cloud modeling theory.

We developed a risk assessment index system for existing tunnels, categorizing risk sources into three areas: hydrogeological conditions, foundation pit construction and tunnel structural bearing capacity. The system includes 11 evaluation indicators linked to these sources, with defined risk level thresholds for each. Using the cloud model, we calculated the membership degree of these indicators to risk levels, replacing traditional membership function formulas. The cloud model’s three digital characteristics (Ex, En and He) account for the randomness and ambiguity between qualitative descriptions and quantitative values, enhancing assessment accuracy. We applied hierarchical analysis to determine the weights of each risk factor and combined these with the membership degrees to evaluate overall risk levels. Engineering applications and model comparisons confirmed the method’s reliability, while sensitivity analysis identified key risk indicators affecting evaluation outcomes, allowing for targeted risk control measures to safeguard existing tunnels during foundation pit construction.

The evaluation results of engineering applications show the same results with the traditional FAHP method, which proves the reliability of the improved method. Furthermore, when comparing the evaluation result vectors between the two methods, it is observed that the outcomes of the improved method are more concentrated on a specific risk level compared to the traditional FAHP. This concentration mitigates the potential for bias in the evaluation results, thereby enhancing their accuracy. Through sensitivity analysis, four indicators were identified to have a significant influence on the evaluation result. After implementing targeted risk control measures, a downgrade in risk level to III was revealed. This aligns with the actual construction circumstances, as no safety incidents occurred in the Line 1 metro tunnel throughout the duration of the pit construction. This confirms the efficacy of the measures taken based on the evaluation results.

The novelty of this study is demonstrated through two key advancements. First, in response to the lack of a mature evaluation index system for risk assessment of existing tunnels during pit construction, the authors have meticulously curated a comprehensive risk evaluation index system. This system provides a valuable reference for the selection of appropriate risk evaluation indices in similar projects. Second, building upon the established index system, the study introduces a cloud model FAHP risk evaluation method. This method automates the generation of the membership degree between indicators and risk levels. The improved method has good reliability for the risk evaluation of existing tunnels, and it can provide decision-making reference for related studies when they carry out risk evaluations of similar projects.

This study aims to propose a calibration method to enhance the positioning accuracy in dual-robot collaborative operations, aiming to address the challenge of drilling hole spacing errors in spacecraft core cabin brackets that require an accuracy of less than 0.5 mm.

Initially, the cooperative error of dual robots is defined. Subsequently, an integrated model is constructed that encompasses the kinematic model errors of the dual robots, as well as the establishment errors of the base and tool frames. A calibration method for optimizing the cooperative accuracy of dual robots is proposed.

The application of the proposed method satisfies the collaborative drilling requirements for the spacecraft core cabin. The average cooperative positioning error of the dual robots was reduced from 0.507 to 0.156 mm, with the maximum value and standard deviation decreasing from 1.020 and 0.202 mm to 0.603 and 0.097 mm, respectively. Drilling experiments conducted on a core cabin simulator demonstrated that after calibration, the maximum hole spacing error was reduced from 1.219 to 0.403 mm, with all spacing errors falling below the 0.5 mm threshold, thus meeting the requirements.

This paper addresses the drilling accuracy requirements for spacecraft core cabins by using a calibration method to reduce the cooperative error of dual robots. The algorithm has been validated through experiments using ER 220 robots, confirming its effectiveness in fulfilling the drilling task requirements.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

The use of recycled coarse aggregate in concrete structures promotes environmental sustainability; however, performance of these structures might be negatively impacted when it is used as a replacement to traditional aggregate. This paper aims to simulate recycled concrete beams strengthened with carbon fiber-reinforced polymer (CFRP), to advance the modeling and use of recycled concrete structures.

To investigate the performance of beams with recycled coarse aggregate concrete (RCAC), finite element models (FEMs) were developed to simulate 12 preloaded RCAC beams, strengthened with two CFRP strengthening schemes. Details of the modeling are provided including the material models, boundary conditions, applied loads, analysis solver, mesh analysis and computational efficiency.

Using FEM, a parametric study was carried out to assess the influence of CFRP thickness on the strengthening efficiency. The FEM provided results in good agreement with those from the experiments with differences and standard deviation not exceeding 11.1% and 3.1%, respectively. It was found that increasing the CFRP laminate thickness improved the load-carrying capacity of the strengthened beams.

The developed models simulate the preloading and loading up to failure with/without CFRP strengthening for the investigated beams. Moreover, the models were validated against the experimental results of 12 beams in terms of crack pattern as well as load, deflection and strain.

This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

This study aims to explore the traditional plant dyeing of Xinjiang Atlas silk fabrics, providing references for the comprehensive utilization of plant dyes in intangible cultural heritage.

The focus of this study is on dyeing experiments of Atlas silk fabrics using safflower extracts, constrained by regional resources. Safflower dry flowers grown in Xinjiang were selected, rinsed with pure water and rubbed. Yellow pigments were removed by adding edible white vinegar. Red pigments from safflower were extracted using an alkaline solution prepared with Populus euphratica ash, a special product of Xinjiang. The extraction rate was analyzed under varying material-to-liquor ratios, pH values, times and temperatures. Direct dyeing process experiments were conducted to obtain different colorimetric L, a, b and K/S values for comparison. Samples with good color development were selected to test the impact of dyeing immersions on color development, and their color fastness, UV protection and antibacterial effects were verified.

The dyeing experiments on silk fabrics confirmed their UV protection capabilities and antibacterial properties, demonstrating effectiveness against E. coli and Staphylococcus aureus. As a major producer of safflower, Xinjiang underscores the significance of safflower as an essential plant dyes on the Silk Road. This study reveals its market potential and suitability for use in the plant dyeing process of Atlas silk, producing vibrant red and pink colors.

The experiments indicated that after removing yellow pigments, the highest extraction rate of red pigment from safflower was achieved at a pH value of 10–11, a temperature of 30°C and an extraction time of 40 min. The best bright red color effect with strong color fastness was obtained with a material-to-liquor ratio of 1:20, a temperature of 40°C and three immersions. The best light pink color effect with strong color fastness was a material-to-liquor ratio of 1:80, a temperature of 30°C and two immersions.