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In the 69 years since the founding of the People’s Republic of China, especially the 40 years since the reform and opening-up, the relationship between urban and rural areas has undergone profound change. When the deepening reform of the urban-rural relationship is entering a critical period, it is necessary to reassess the evolution of the urban-rural relationship in China and draw a picture for that relationship in the future. The paper aims to discuss these issues.

This paper combs the policies on the urban and rural development since the founding of the People’s Republic of China, and analyzes macro data on the industries, population, personal income, and other aspects.

The study found that this urbanism affects individuals’ lives and the choices of society through the will of the state, and then provides feedback at the whole level of social values.

This paper divides the evolution of China’s urban-rural relationship into two major stages – nurturing cities with rural areas and leading rural areas with cities, which are then subdivided into five periods. The features of the relationship between the urban and rural areas in different periods are analyzed, and the future development of urban-rural relations is also considered.

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.

Based on imprinting theory and episodic future thinking, this paper aims to study how CEOs’ attributes and experiences inform innovation in tourism and hospitality businesses. It also explores ways to quantify innovation in this sector.

The authors quantitatively analysed innovation in tourism and hospitality using extensive data from companies’ annual reports. They further adopted multivariate regression to test how CEOs’ experience affects enterprise innovation.

Results demonstrate that CEOs’ academic education and rich work experience can promote corporate innovation. The authors also identified a mediating role of the tone of narrative disclosure in annual reports between CEOs’ academic education and corporate innovation. The imprinting effects of career experience and educational experience appear both independent and interactive.

CEOs are more inclined to engage in corporate innovation when influenced by the combined imprinting effects of strategic management training and work experience. Additionally, leaders should consider how communication styles indirectly influence innovation activities.

This paper introduces an integrated perspective that blends imprinting theory and episodic future thinking to bridge knowledge gaps regarding the interaction of CEOs’ past experiences. This work enhances understanding of how CEOs’ imprinted experiences, together with their capacity for envisioning future scenarios, can drive corporate innovation.

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.

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 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.

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.