The advent of the Internet Plus era, digital technologies, and the digital economy has instigated profound transformations in the commercial landscape, particularly evident in the systematic reshaping of the Digital Business Ecosystem (DBE), encompassing innovations in business models, norms of commercial conduct, and the exploration of business value. This paper delves into the panoramic view of digital business operations of typical companies to uncover the fundamental structural framework of digital commerce. Through deductive reasoning and drawing upon the theoretical framework of natural niche, we construct a niche model for the digital business ecosystem, thereby achieving a bionic deconstruction of the digital business ecosystem. The significance of this research lies in offering a novel research perspective for enterprises, economic regulatory bodies, and scholars in the field of business management, proposing a systemic approach rooted in niche theory models to competition. This approach provides a fresh theoretical framework for enterprises to devise their own ecological and sustainable development strategies. The key findings are as follows: (1) Most business firms establish competitive advantages by constructing commercial cloud platforms that facilitate internal digital transformation and enable digital synergy with external economic entities; (2) Within the digital business ecosystem, enterprises extend their digital capabilities externally through four modalities: data development, data application, data services, and data manufacturing. Externally, six primary forces and roles shape the ecosystem: suppliers, governments, social institutions, consumers, as well as external and internal industry players; (3) The digital business niche is a multidimensional and hyper volumetric relationship positioning between enterprises and the digital business environment. The niche factors include six dimensions: market, personnel, resources, social relationships, technology, and institutions; (4) Given limited ecological factors, the non-exclusivity between static resource allocation and dynamic technological investments in digital enablement leads to the generalization of property rights boundaries and industrial values within the digital business ecosystem. Consequently, this fosters extensive business applications and diversified business models, thereby resulting in less competition and more cooperation, symbiosis, and complementarity within the digital business niche.

In the new era, an important component of China’s social governance system construction is to strengthen and innovate social governance to improve the ability and level of social governance in China. To ensure the long-term stability of the country and the well-being of the vast majority of the people, it is necessary to be adept at strengthening social governance, continuously improve and improve the governance system that is suitable for the development of modern society with scientific thinking methods, and enhance the level and capacity of governance in China. Based on this, this paper discusses how to promote the innovation of social governance in the digital age, and proposes innovative ideas on the model of social organization governance under the guidance of <Economic Diversification Plan for Macao SAR (2024–2028)>.

This research explores the role of digital economy in driving agricultural development in the BIMSTEC region, which includes Thailand, Myanmar, Sri Lanka, Nepal, India, Bangladesh and Bhutan (with Bhutan excluded due to data limitations) with a particular focus on mobile technologies, computing capacity and internet connectivity which were the most readily available data points for BIMSTEC. Using a combination of document analysis, and panel data analysis with the data covering 10 years (2012–2021), the study examines the interplay of key digital technologies with agricultural growth while controlling for factors including water usage, fertilizer consumption, and land temperature and agricultural land area. The analysis incorporates additional variables such as infrastructure development, credit to agriculture, investment in agricultural research, and education level. The findings reveal a strong positive correlation between mobile technology, Internet and computing capacity in BIMSTEC. This study underscores that digital tools are pivotal in enhancing agricultural productivity, yet their impact is significantly combined with investment in infrastructure and education. This study suggests that digital solutions, when strategically integrated with broader socio-economic factors can effectively challenges in developing countries, particularly in rural and underserved regions. This research contributes to the growing body of literature on digital economy in agriculture, highlighting how digital technologies can foster agricultural productivity in developing countries.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

In order to strengthen the study of soil-landscape relationships in mountain areas, a digital soil mapping approach based on fuzzy set theory was applied. Initially, soil properties were estimated with the regression kriging (RK) method, combining soil data and auxiliary information derived from a digital elevation model (DEM) and satellite images. Subsequently, the grouping of soil properties in raster format was performed with the fuzzy c-means (FCM) algorithm, whose final product resulted in a fuzzy soil class variation model at a semi-detailed scale. The validation of the model showed an overall reliability of 88% and a Kappa index of 84%, which shows the usefulness of fuzzy clustering in the evaluation of soil-landscape relationships and in the correlation with soil taxonomic categories.