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With rapid urbanization, cities are facing various ecological and environmental problems. Living in harmony with nature is more important than ever. This paper aims to evaluate the ecosystem and ecological features of Azheke village, a key component of the Hani Rice Terraces World Cultural Heritage in China. The focus is on exploring effective ways to improve the relationship between humans and the natural environment through urban design in order to create a livable and sustainable city that can promote the development of sustainable smart urban ecology design.

This study conducted a systematic literature review to answer the following research questions: (1) How does Azheke design achieve harmony between humans and nature? (2) What are the effective approaches to improve the relationship between humans and nature within urban ecosystems? (3) How can urban design learn and integrate from Azheke’s ecological features to improve the relationship between humans and nature?

Azheke sustains long-term human-nature harmony through traditional ecological knowledge (TEK) and efficient natural resource use. By incorporating biophilic design and nature-based solutions from Azheke, along with biodiversity-friendly urban planning, we can boost urban ecosystem health and create unique Azheke-inspired urban designs.

This research primarily focuses on the human-nature relationship, exploring design strategies based on biodiversity without delving into the interactions between other components of urban ecosystems, such as social-cultural and economic components.

This paper provides a new perspective and strategies for developing sustainable and smart urban ecology design. These findings can provide theoretical references for urban planners, designers and decision-makers.

Information distortion affects the perception of quality, which, in turn, influences investment decisions and determines the pledge results of fundraising. This study combines signalling theory with persuasion theory to empirically study the effects of linguistic information distortion from fraudulent cues on a crowdfunding campaign's fundraising outcomes using text analytics, with implications for entrepreneurs, platforms and investors.

This study empirically analyzes 328,974 crowdfunding projects from the Kickstarter platform. Information distortion is detected using four indicators, based on text mining analytics. An econometric model is built to estimate the impact of information distortion, while the predictive power of the information distortion is detected through machine learning.

The results inform that distortion in the blurb, detailed description and reward statement dampen a campaign's success, but embellishing the entrepreneur's biography enhances the success of financing. Furthermore, information distortion exhibits a significant inverted U-shaped influence. The effect of the interaction terms suggests that campaigns with high pledge goals are more sensitive to information distortion, and that native-speaking entrepreneurs are adept at applying linguistic skills to promote the campaign.

This study provides a linguistic method to detect the influence of information distortion on crowdfunding campaigns. Further, the study offers some practical suggestions for entrepreneurs on how to generate attractive narratives, and contributes to the investor's decision-making and informs the platform's promotion strategy.

The effect of board intellectual capital on non-profit organizational performance in non-western, less developed economies has been an important yet under-researched area. Given that the institutional and business relationships of a board account for the majority of board intellectual capital, the purpose of this paper is to fill the previously mentioned research gap by addressing how the interactions of the two relationships of board directors influence Chinese philanthropic foundation performance.

Following Creswell's (2014) explanatory sequential mixed-methodology, a qualitative study (Study 1) was first conducted to pre-test the assumptions, and then a quantitative study (Study 2) was carried out based on a secondary database of 1,405 Chinese philanthropic foundations to further examine the hypotheses. Several regression models were built for analyzing the results.

Study 1 confirmed that Chinese philanthropic foundations gained greater revenues and hosted more public welfare activities by leveraging the reinforcing or complementary effects of board directors' intellectual capital to improve organizational performance. Study 2 further examined the hypotheses that the interactions of intellectual capital increased the total revenue and public welfare expenditure of the foundations; however, significant positive relationships were only identified in foundations at the local level, and no significant associations were found in those at the national level.

The research indicates that the intellectual capital of board directors may influence the performance of their philanthropic foundations. Thus, Chinese philanthropic foundations should be more aware of the importance of this influence when determining which candidates will join the board.

The study makes significant contributions to the existing knowledge of the development of non-governmental organizations; it incorporates the resource dependence theory and agency theory into understanding how the intricate interactions between the institutional and business relationships of board directors affect foundation performance and how the jurisdiction affiliations act as a boundary condition for such relationships in a non-western setting such as China.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

The purpose of this paper is to develop a high-performance composite emulsion cement waterproof coating. The coating has excellent durability and is effective in protecting cement mortar substrates from harmful ions.

The polymer cement waterproof coatings with different emulsion compounding ratios were tested for mechanical properties and water resistance after alkali immersion, water immersion, thermal aging and UV aging, and the coatings were analyzed by infrared spectroscopy after aging to evaluate its durability. Meanwhile, the coating that presents favorable durability was applied to cement mortar test blocks. The protective effect of the coating on the test blocks was tested by immersion method, and X-ray diffraction analysis was performed on the eroded test blocks.

The coating with neoprene latex/acrylate latex weight ratio of 90/10 presents favorable durability and has superior overall performance. Besides, when it is applied to cement mortar blocks, the coatings effectively reduced the erosive effect of harmful ions on cement mortar blocks, resulting in much lower mass change ratios and less internal structural damage of the blocks significantly.

The obtained coating will be of great application potential for use in building waterproofing construction. Moreover, the coating can practically prevent chloride ions and sulfate ions from penetrating cement-based materials.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

Sustainable consumption is a crucial route to sustainable tourism. Hence, the purpose of this study is to investigate the individual and combined effect of social learning and eco-labels on the green hotel selection.

This study used a between-subject online experimental method with 199 respondents, which is used only in around 15% of published tourism articles. Additionally, this method provided reliability and control features.

Social learning and eco-labels individually could not influence tourists to select green hotels. However, the interaction of positive performance social learning with eco-labels could potentially influence tourists' green hotel selection. Therefore, internalizing green hotel performance from trusted sources and external validation bring behavioral changes among tourists to select green hotels. This study offers a new social learning-based model for understanding sustainable consumption.

This study can use various other types of social influences and can be expanded to other green settings.

This study provides policy and promotion formulation insights to hotel managers and sustainable tourism promoters to market green hotels.

Away from the traditional theory, this study bridges the gap between social learning of performance from relatable sources, assurance from governing entities and sustainable consumption. The interaction effect of social influence and eco-label on green hotel selection is a novel finding. Also, this study introduces various levels of social learning to the discussion of sustainable consumption.

该研究对 199 名受访者使用了主题间在线实验方法, 仅在大约 15% 的已发表旅游文章中使用。此外, 这种方法提供了可靠性和控制功能。

可持续消费是可持续旅游的重要途径。因此, 本研究调查了社会学习和生态标签对绿色酒店选择的个体和综合影响。

单独的社会学习和生态标签不能影响游客选择绿色酒店。然而, 积极绩效社会学习与生态标签的相互作用可能会影响游客对绿色酒店的选择。因此, 从可信赖的来源和外部验证中内化绿色酒店绩效, 会带来游客选择绿色酒店的行为变化。该研究为理解可持续消费提供了一种新的基于社会学习的模型。

该研究可以使用各种其他类型的社会影响, 并可以扩展到其他绿色环境。

它为酒店经理和可持续旅游推广者提供了政策和促销制定的见解, 以推销绿色酒店。

与传统理论不同, 本文填补了相关来源的绩效社会学习、管理实体的保证和可持续消费之间的研究空白。社会影响和生态标签对绿色酒店选择的交互作用是一个新的发现。此外, 它还将不同层次的社会学习引入到可持续消费的讨论中。

El estudio utilizó un método experimental online entre sujetos con 199 encuestados, utilizado sólo en alrededor del 15% de los artículos de turismo publicados. Además, este método ofrecía características de fiabilidad y control.

El consumo sostenible es una vía crucial para el turismo sostenible. Por ello, este estudio investiga el efecto individual y combinado del aprendizaje social y las etiquetas ecológicas en la selección de hoteles ecológicos.

El aprendizaje social y las ecoetiquetas por separado no pudieron influir en los turistas para que seleccionaran hoteles ecológicos. Sin embargo, la interacción del aprendizaje social del rendimiento positivo con las ecoetiquetas podría influir potencialmente en la selección de hoteles ecológicos por parte de los turistas. Por lo tanto, la interiorización del rendimiento de los hoteles ecológicos a partir de fuentes de confianza y la validación externa provocan cambios de comportamiento entre los turistas para seleccionar hoteles ecológicos. El estudio ofrece un nuevo modelo basado en el aprendizaje social para entender el consumo sostenible.

El estudio puede utilizar otros tipos de influencias sociales y puede ampliarse a otros entornos ecológicos.

Proporciona ideas para la formulación de políticas y promociones a los directores de hoteles y a los promotores del turismo sostenible para comercializar hoteles ecológicos.

Se aleja de la teoría tradicional y tiende un puente entre el aprendizaje social de los resultados a partir de fuentes relacionadas, la garantía de las entidades gobernantes y el consumo sostenible. El efecto de la interacción entre la influencia social y la etiqueta ecológica en la selección de hoteles ecológicos es un hallazgo novedoso. Además, introduce varios niveles de aprendizaje social en el debate sobre el consumo sostenible.

This study aims to explore the complex relationship between leadership and organizational knowledge sharing by investigating the moderating role of exchange ideology on the relation between transformational leadership in attributed charisma and knowledge sharing and the influence of attributed charisma and knowledge sharing on task performance. The influence of leadership in organizational knowledge sharing process has been gradually highlighted.

Based on the review of relevant literature and survey, a structural equation model considering four factors in the model together is now constructed and provides four hypotheses which can be verified. Self-completed questionnaires were collected from 163 students in the context of a graduate class in China.

The findings illustrate the relationship between leadership theory and knowledge sharing from a perspective of social exchange theory. In particular, results show that both transformational leadership and knowledge sharing have positive impacts to task performance and for individuals with low exchange ideology the positive influence from attributed charisma to knowledge sharing is stronger.

This research introduces exchange ideology as a moderator and explains the complex relationship between transformational leadership and knowledge sharing with sufficient proof. Transformational leadership in attributed charisma is more effective to those individuals with low exchange ideology in facilitating their knowledge effort. This paper can be theoretically and practically helpful to researchers and enterprise leaders in organizational knowledge management.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance.

The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic.

The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP.

This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further.

The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance.

The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics.