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The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.

Using the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.

The results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.

Credit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

Based on the density dependence theory, this paper attempts to explore how two types of interdependence among firms located in the same national high-tech zones (NHTZs) – mutualism and competition – affect entrepreneurship in the NHTZs. The authors suggest that increasing firm density can help enhance legitimacy and form mutual networks. However, as the competition becomes fierce, the above positive relationship will weaken when the firm density exceeds a certain level. In addition, the authors are interested in whether the age of NHTZs would affect their sensitivity to legitimacy and competition and whether firm density affects entrepreneurship.

This article formulates two hypotheses from the theoretical deduction. The hypotheses are examined using the ordinary least squares (OLS) regression with a unique, unbalanced panel dataset of Chinese NHTZs spanning from 2014 to 2021. Considering potential endogeneity risk among the variables, the authors attempt to lag variables and ultimately find the results are still robust.

Drawing upon the density dependence theory, the empirical results show firm density is conducive to promoting entrepreneurship, while the positive relationship between community density and NHTZs' entrepreneurship gradually weakens as the firm density surpasses a certain level. The dynamics between mutualism and competition have different impacts on NHTZs' entrepreneurship. In addition, the results demonstrate that the linkage between firm interdependence and entrepreneurship is stronger for younger NHTZs. Firm density has an impact on entrepreneurship through legitimacy and excessive competition effects.

On the one hand, the research period of this paper is 2014–2021, as the China Torch Statistical Yearbook only started to publish operating revenues in 2014, so the data period of this paper is relatively short. More research can be done in the future when more data is disclosed. On the other hand, the qualitative analysis cannot be conducted because of the limited data and materials. In future research, the qualitative analysis of entrepreneurial activities in NHTZs, such as questionnaires or case studies, needs to be supplemented, which will be an interesting direction.

Most existing research has not distinguished the differences between NHTZs (Wang et al., 2019), especially the differences in legitimacy and access to resources caused by the age of NHTZs. This article considers the heterogeneity between NHTZs, which helps to provide theoretical and practical evidence for a transition economy like China to make trade-off decisions on balancing absorbing new entrants with promoting the efficient allocation of resources based on the density and age of NHTZs.

Drawing upon density dependency theory, this paper enriches the literature on agglomeration and entrepreneurship with a new perspective and extends the study to NHTZs.

First, this paper provides new evidence on how agglomeration affects entrepreneurship from an ecological perspective with the help of mutualism and competition interdependence. Most studies have explored the role of agglomeration in entrepreneurship, focussing on social networks, knowledge spillovers or resource endowments (Acs et al., 2013; Capozza et al., 2018; Yu, 2020). Drawing upon density dependency theory, this paper enriches the literature on agglomeration and entrepreneurship with a new perspective and extends the study to NHTZs. Second, the emphasis of science parks has been primarily on qualitative or case studies (Salvador et al., 2013; Guo and Verdini, 2015; Xie et al., 2018). We have diversified the quantitative research between agglomeration and entrepreneurship by using panel data from Chinese NHTZs from 2014 to 2021. Third, most existing research has not distinguished the differences between NHTZs (Wang et al., 2019), especially the differences in legitimacy and access to resources caused by the age of NHTZs. This article considers the heterogeneity between NHTZs, which helps to provide theoretical and practical evidence for a transition economy like China to make trade-off decisions on balancing absorbing new entrants with promoting the efficient allocation of resources based on the density and age of NHTZs. Finally, this paper meticulously investigates the profound influence and underlying mechanisms of firm density within NHTZs on entrepreneurship. It discerns two distinct mechanisms at play: the legitimacy effect and the impact of excessive competition resulting from firm density. This comprehensive analysis significantly contributes to our comprehension of the intricate interplay between firm density and entrepreneurship, shedding light on the dynamics of competition and mutual benefits.

This study aims to investigate the factors influencing the social responsibility performance of Belt and Road Initiative (BRI) megaprojects. Specifically, it examines the role of isomorphic pressures and the joint influence of perceived benefits and top management support on megaproject social responsibility performance (MSRP).

Drawing from institutional theory, social exchange theory, and top management literature, this study established a conceptual model featuring eleven hypotheses. Subsequently, a questionnaire survey was administered to collect data from 238 actively engaged participants in BRI megaprojects. Structural Equation Modelling was utilised to analyse the data.

The empirical findings indicate that mimetic and coercive pressures positively influence MSRP. Perceived benefits and top management support significantly enhance MSRP. Moreover, perceived benefits and top management support partially mediate the effects of coercive and mimetic pressures. However, when it comes to normative pressures, their impact on MSRP is solely channelled through the support of top management.

This study is one of the early endeavours to explore the factors influencing the social responsibility performance of BRI megaprojects. It sheds light on the interplay between external pressures and internal factors in shaping social responsibility efforts in these projects. These findings are of particular significance for BRI actors and stakeholders, offering guidance for enhancing social responsibility strategies within the context of BRI megaprojects.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This paper aims to identify and provide a theoretical explanation for the barriers that hinder the adoption of emerging technologies in the architecture, engineering and construction industry, irrespective of the company’s size, specialization or geographical location. In addition, the paper proposes potential areas for future research in this domain.

A list of barriers hindering the adoption of emerging technologies was identified and clarified using a systematic literature review of various scientific sources.

Twenty-five barriers were recognized and explained and some suggestions for future research studies were provided.

The barriers related to a specific country or region or to a specific technology were excluded.

By providing a deeper comprehension of the barriers hindering the adoption of emerging technologies, this review is expected to encourage their adoption in the industry. Furthermore, it could prove valuable in devising effective strategies for the successful implementation of these technologies.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

From the perspective of local government tax administration, the impact of geographic dispersion on the corporate tax burden is investigated in this paper.

Using unbalanced panel data with a sample of listed companies from 2003 to 2020 in China, this paper focuses on the effect of geographic dispersion on corporate tax burden and the mechanisms.

It is found that corporate tax burden is positively related to geographic dispersion. It is also found that geographic dispersion affects the corporate tax burden by increasing the effort of local government tax administration. In addition, the relation between geographic dispersion and corporate tax burden is more pronounced for local SOEs prior to the implementation of Golden Tax Project III and in cases where local governments face stronger financial pressure to obtain revenue.

This study has important implications for the promotion of the coordinated development of the regional economy, as well as the legalization, modernization and informatization of tax administration.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.