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The purpose of this paper is to explore the factors influencing customers’ willingness to participate in virtual brand community’s value co-creation and help companies better operating the virtual brand community.

Based on social cognitive theory and the features of the virtual brand community, this paper constructed a model of factors influencing customers’ willingness to participate in virtual brand community’s value co-creation. Then this paper quantitatively analyzed the mediating effect and the moderating effect.

The empirical analysis came to the following conclusions: first, in virtual brand communities, customers’ willingness to participate in value co-creation would be influenced by subject factors, environment factors, brand factors and perceived value factor. Second, customer involvement is an important moderator. The more involved the customer is, the more he/she will rely on the virtual brand community. Particularly, customer involvement has a positive moderating effect on the influence of subject factors, while it has a negative moderating effect on the influence of community experience and community trust. Third, perceived value plays a significant mediating role between subject factors and customers’ willingness to participate in value co-creation.

The results of this study can help companies better understand the influence of external factors like environment and brand so that they can better operate the virtual brand community and encourage customers to contribute to the development of the community and the brand.

Most of the existing studies focused on the formation of virtual brand communities and customers’ participation behaviors, but there is limited research focusing on what contributes to customers’ participation in value co-creation of virtual brand communities. This study, therefore, attempts to bridge the research gap.

The purpose of this study is to propose two additions to the existing IS success model to influence usefulness and satisfaction, and ultimately customer loyalty. This study posits that process quality and collaboration quality will significantly influence perceptions on satisfaction, since both considerations are expected to improve interactions between the organization and its customers.

The developed research model was tested in the context of B2B e‐commerce users, employing structural equation modelling‐partial least square to analyze and test the research hypotheses.

Results show that adding process and collaboration quality to the model affects the way system, information, and service quality influences usefulness and satisfaction, suggesting a shift in perspectives and priorities when it comes to B2B e‐commerce transactions. Furthermore, results show that process and collaboration quality have significant effects on usefulness and satisfaction, reinforcing the objective of using B2B e‐commerce systems across supply chain members.

This highlights the importance of improving business processes and enhancing collaboration activities, implying that managers should focus their efforts into enhancing customer service on top of system functionalities and features via B2B to better work with their supply chain partners. However, this study is limited in the context of a Taiwanese supply chain, suggesting that further research consider its applicability in different contexts, culturally and geographically, especially given the fact that B2B e‐commerce enables cross‐border business operations.

This study also extends the IS success model, especially in the context of B2B e‐commerce, by emphasizing behavioural considerations on top of the functional issues present in any information systems.

The purpose of this paper is to conduct the synthesization of LiFePO₄-C (LFP-C) with fine particle size and enhanced electrochemical performance as the positive electrode material for Li-ion capacitors (LICs) with neutral aqueous electrolyte.

LFP-C was prepared by using polyethylene glycol (PEG) as a grain growth inhibitor, and the effects of the calcination temperature and PEG content on the structure and morphology of LFP-C were investigated. LICs using environment-friendly, safe and low-cost LiNO₃ aqueous electrolyte were assembled with LFP-C as the positive electrode and active carbon as the negative electrode. The electrochemical performances of LFP-C and LICs were studied.

The results show that the particle size of LFP-C decreases significantly through the introduction of PEG. Cyclic voltammetry results show that the LFP-C prepared at 550°C with 1.0 g PEG exhibits the highest C_pe of 725 F/g at the scanning rate of 5 mA/s. Compared to LFP prepared without PEG, the electrochemical performance of optimized LFP-C dramatically increases due to the decrease of the particle size. Moreover, the LIC assembled with the optimized LFP-C exhibits excellent electrochemical performances. The LIC maintains about 91.3 per cent of its initial C_ps after 200 cycles which shows a good cycling performance.

The LFP-C is the suitable positive electrode material for LICs with neutral aqueous electrolyte. LICs can be used in the field of automobiles and can solve the problems of energy shortage and environmental pollution.

Both the LFP-C with fine particle size and its optimal LIC using environment-friendly, safe and low-cost LiNO₃ aqueous electrolyte own good electrochemical performances.

The prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.

First, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.

The proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.

The main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

This paper aims to examine the impact of the COVID-19 infodemic on the public’s online information behaviour, offering insights critical for shaping effective informational responses in future public health emergencies.

This paper uses a structured online survey with 27 targeted questions using a five-point Likert scale to measure eight variables. Data analysis is conducted through structural equation modelling on 307 valid responses to rigorously test the research hypotheses.

This paper indicates that information quality significantly impacts the public’s capacity to select, share and use online information. Additionally, the comprehensibility of information plays a crucial role in shaping the public’s behaviours in terms of online information exchange and usage. The credibility of information sources emerges as a key determinant influencing the public’s online information selection, exchange and utilization behaviour. Moreover, social influence exerts a substantial effect on the public’s online information selection, acquisition, exchange and utilization behaviour. These findings highlight the presence of universality and sociality, mediation and guidance, as well as the purposefulness and selectivity performed by the public’s online information behaviour during an infodemic.

This paper introduces a novel research model for assessing the influence and identifies the patterns of the public’s online information behaviour during the COVID-19 infodemic. The findings have significant implications for developing strategies to tackle information dissemination challenges in future major public health emergencies.

The transient loads on the spherical hybrid sliding bearings (SHSBs) rotor system during the process of accelerating to stable speed are related to time, which exhibits a complex transient response of the rotor dynamics. The current study of the shaft center trajectory of the SHSBs rotor system is based on the assumption that the rotational speed is constant, which cannot truly reflect the trajectory of the rotor during operation. The purpose of this paper truly reflects the trajectory of the rotor and further investigates the stability of the rotor system during acceleration of SHSBs.

The model for accelerated rotor dynamics of SHSBs is established. The model is efficiently solved based on the fourth-order Runge–Kutta method and then to obtain the shaft center trajectory of the rotor during acceleration.

Results show that the bearing should choose larger angular acceleration in the acceleration process from startup to the working speed; rotor system is more stable. With the target rotational speed increasing, the changes in the shaft trajectory of the acceleration process are becoming more complex, resulting in more time required for the bearing stability. When considering the stability of the rotor system during acceleration, the rotor equations of motion provide a feasible solution for the simulation of bearing rotor system.

The study can simulate the running stability of the shaft system from startup to the working speed in this process, which provides theoretical guidance for the stability of the rotor system of the SHSBs in the acceleration process.

The purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.

This study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.

The results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.

The results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.

The aim of this paper is to study the tribological performance and self-repairing performance of surface-modified nanoscale serpentine powders as lubricant additives in the mineral base oil (5-CST).

Fourier transform infrared spectroscopy spectra and thermo-gravimetric analysis of both modified and unmodified serpentine were performed to analyse their grafting ratio and suspension after modified using a long-chain naphthene aliphatic acid. The tribological properties of surface-modified serpentine as lubricant additives in 5-CST were evaluated and the worn surfaces were investigated by X-ray photoelectron spectroscopy (XPS) and X-ray absorption near edge structure (XANES).

The results show that the serpentine particles have high grafting ratio, improving the dispersity in 5-CST. When the serpentine concentration of 1.00 weight per cent is used as additives in 5-CST, friction coefficient reduces by 14.80 per cent under 294 N and wear scar diameter (WSD) decreases by 11.82 per cent. The results of X-ray absorption near edge structure and XANES show that the adsorption and tribochemical reactions occur to form self-repairing lubrication films.

The paper illustrates a tribofilm form on the rubbed surface, which is responsible for the decrease in friction and wear, mainly containing iron oxides, silicon oxides, magnesium oxides and organic compounds. The results are useful for further applications in advanced environmental friendly lubricating oils and additives.

The purpose of this study is to look into the concept of blockchain technology which stores data in a distributed, tamper-resistant setting and how this technology may affect the future library and information (LI) centers’ operations and policy. It explores the potential benefits and limitations of this technology, for LI centers are the core objectives of this paper.

A systematic review of both academic and practitioner literature was conducted. It followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (2020) standards.

The review shows that the decentralized nature of blockchain will transform the work in libraries and improve the inter-communication among communities. It can provide better privacy of user data thus increasing collaboration. However, there might be technological, financial, legal and social challenges that may act as hurdles to the realization of these goals in the true sense. The overall result shows that by keeping data updated, libraries can evaluate blockchain opportunities and make the best use of this technology.

Given the huge investments by industry, academic research that explores potential ramifications and supports libraries is required. The adoption of blockchain was investigated in this study from a variety of perspectives for LI center’s while also providing directions for future research.

It is evident that globally there is progress in blockchain technology development in terms of research and adoption in many industries, but the intention to adopt blockchain by libraries is still a fantasy. The lack of empirical evidence in the existing literature justifies the gap that needs to be filled. This systematic review attempts to fill the gaps in the existing knowledge which may serve as a reference for future initiatives in this field.

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.