This research aims to solve the research problems regarding the most important value of an object in the form of the wedangan phenomenon. This research objectives to expose the superiority of the communities’ food consumption tradition in the form of wedangan. This research belongs to a qualitative study and uses ethnomethodology as an initial approach. It is because the initial data findings are in the form of an indexical conversation that explicitly refers to the concept of wedangan. The concept refers to wedangan in real life, which is in the form of eating and drinking activities while chatting. The research findings are: 1) the most profound structure of wedangan’s tradition is food provision and food eating; 2) wedangan accommodates three forms (food stall, street food, and restaurant); 3) wedangan also accommodates three food values (delightful, useful, and meritorious); and 4) there is an egalitarian consumption pattern in wedangan, people regardless their social class visiting the same place, eat the same food, being simple and be ordinary (or usually we call it as food marriage). Wedangan is a social activity with advantages from a social, economic, and political perspective. Therefore, this phenomenon requires more serious attention from the government.

The purpose of the article is to examine the changes in cross-border cooperation between Vietnam and China as a result of the development and connectivity of cross-border infrastructure between the two countries. This article is based on a mixed-methods study that includes desk research and surveys. The article explains how the two countries’ approaches to border shifted from ‘barrier’ to the border of ‘connectivity’. Accordingly, the article examines the changes in border management cooperation between the two countries, which serves as a vital basis for cross-border development cooperation. Furthermore, the article examines the perceptions of the two countries regarding the development and connectivity of cross-border infrastructure for comprehensive cooperation between the two countries and beyond. At the same time, the article examines how the two countries promote the development and connectivity of cross-border infrastructure, both hard and soft. The article also examined some initial results and some issues facing the two countries. The paper concludes with some findings. In particular, the article concludes that increased border connectivity will encourage cross-border cooperation and integration between the two countries and help to alleviate security concerns. Although the two countries have made efforts to open their borders, in the transition from a border of ‘barriers’ to a border of ‘connectivity’ remain partly to Vietnamese people’s memories of the 1979 Sino-Vietnamese border war, as well as the impact of the two countries’ unresolved South China Sea disputes. However, Vietnam also tries to promote cross-border cooperation within a controllable level.

Regional cooperation stands as a key strategy to address intense economic competition and formidable local governance challenges. Successful regional collaborations are typically founded on the basis of institutional similarity, which also serves as the starting point for a multitude of related theoretical studies. Consequently, the regional cooperation within the context of institutional conflicts has been overlooked. This paper aims to explore the process of regional cooperation against the backdrop of conflicts, using the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as a case study and analyzing it from the perspective of the sociology of knowledge. The article posits that conflicts can stimulate interactions among various actors, foster the generation of local knowledge, and propel specific cooperative practices. Moreover, local and central governments, grounded in local knowledge and universal managerial insights, continuously authenticate and propagate local innovations, establishing guiding policies and, consequently, producing rational knowledge. The accumulation of such knowledge has not only strengthened civilian cooperation but also facilitated broader collaborative efforts. The study reveals that despite the GBA’s remarkable achievements in cooperation, challenges persist: on the one hand, there are issues with the government’s process of rational knowledge production and the quality of knowledge itself; on the other hand, excessive governmental dominance may suppress the production and application of local knowledge. Therefore, refining the knowledge production mechanism is especially critical. The findings of this paper uncover the mechanisms of regional cooperation amidst institutional conflicts and deepen our understanding of regional collaboration and cross-border governance.

State-owned enterprises (SOEs) manage significant portion of world economy, including in the developing countries. SOEs are expected to be active and play significant role in improving the country’s economic performance and welfare through enhancing innovation performance. However, closed innovation process and lack of collaboration hinders SOEs to reach satisfying innovation performance level. This paper explores the construction and role of innovation ecosystem in the strategic entrepreneurship process of SOEs, of which is represented by dynamic capability framework, business model innovation, and collaborative advantage. Based on the analysis, this paper concluded that the collaboration between actors in the Innovation Ecosystem (IE) has positive effect to strengthening SOE’s Sensing Capabilities (SC) related to the process of exploring and identifying innovation opportunities. The increase of Sensing Capabilities (SC) will play significant role as input or antecedent on formulating proactive Innovation Strategy (IS) in orchestrating SOE’s innovation process. SOEs which has implementing proactive Innovation Strategy (IS) will be able to build collaboration and finding right Business Model Innovation (BMI). Finally, by building collaboration with other actors through the innovative business model has significant role to increase SOE’s Collaborative Advantage (CA), which considered as a proxy for competitiveness of SOEs.

To achieve the energy transition and carbon neutrality targets, governments have implemented multiple policies to incentivize electricity suppliers to invest in renewable energy. Considering different government policies, we construct a renewable energy supply chain consisting of electricity suppliers and electricity retailers. We then explore the impact of four policies on electricity suppliers’ renewable energy investments, environmental impacts, and social welfare. We validated the results based on data from Wuxi, Jiangsu Province, China. The results show that government subsidy policies are more effective in promoting electricity suppliers to invest in renewable energy as consumer preferences increase, while no-government policies are the least effective. We also show that electricity suppliers are most profitable under the government subsidy policy and least profitable under the carbon cap-and-trade policy. Besides, our results indicate that social welfare is the worst under the carbon cap-and-trade policy. With the increase in carbon intensity and renewable energy quota, social welfare is the highest under the subsidy policy. However, the social welfare under the renewable energy portfolio standard is optimal when the renewable energy quota is low.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.