The urgency of adapting urban areas to the increasing impacts of climate change has prompted the scientific community to seek new approaches in partnership with public entities and civil society organizations. In Malaysia, Penang Island has developed a nature-based urban climate adaptation program (PNBCAP) seeking to increase urban resilience, reduce urban heat and flooding, strengthening social resilience, and build institutional capacity. The project includes a strong knowledge transfer component focused on encouraging other cities in the country to develop and implement adaptation policies, projects, and initiatives. This research develops a model adopting the most efficient processes to accelerate the transfer of knowledge to promote urban adaptation based on the PNBCAP. The methodology is developed based on a review of literature focused on innovation systems and change theories. The integration of success strategies in adaptation contributes to informing the creation of solutions around the alliance of local, state, and national government agencies, scientific institutions, and civil society organizations, in a new framework designated the Malaysian Adaptation Sharing Hub (MASH). MASH is structured in 3-steps and will function as an accelerator for the implementation of urban climate adaptation policies, with the target of creating 2 new adaptation-related policies to be adopted annually by each city member, based on knowledge gathered in the PNBCAP. It is concluded that, to speed up urban adaptation, it is necessary to reinforce and promote the sharing of knowledge resulting from or associated with pilot projects.

This study, drawing on the Knowledge-Based View (KBV) and Contingency Theory, explores how analyzer strategic orientation, learning capability, technical innovation, administrative innovation, and SME growth and learning effectiveness are interrelated. Analyzing cross-sectional data from 407 founders, cofounders, and managers of trade and service SMEs in Vietnam’s Southeast Key Economic Region through PLS-SEM, the research demonstrates that analyzer orientation positively impacts both technical and administrative innovation, thereby bolstering SME growth and learning effectiveness. However, learning capability does not significantly impact technical innovation or growth and learning effectiveness. Instead, learning capability negatively affects administrative innovation. Notably, technical and administrative innovations act as mediators between analyzer orientation and SME growth and learning effectiveness. The study provides practical insights tailored for SMEs navigating dynamic market environments like Vietnam, enriching theoretical understanding of SME strategic management within the trade and service sector.

The mining sector faces a complex dilemma as an economic development agent through social upliftment in places where mining corporations operate. Resource extraction is destructive and non-renewable, making it dirty and unsustainable. To ensure corporate sustainability, this paper examines the effects of knowledge management (KM), organizational learning (OL), and innovation capability (IC) on Indonesian coal mining’s organizational performance (OP). We used factor and path analysis to examine the relationships between the above constructs. After forming a conceptual model, principal component analysis validated the factor structure of a collection of observed variables. Path analysis examined the theories. The hypothesized framework was confirmed, indicating a positive association between constructs. However, due to mining industry peculiarities, IC does not affect organizational performance (OP). This study supports the importance of utilizing people and their relevant skills to improve operational performance. The findings have implications for managers of coal mining enterprises, as they suggest that KM and OL are critical drivers of OP. Managers should focus on creating an environment that facilitates knowledge sharing and learning, as this will help improve their organizations’ performance.

This study analyzes the social and individual stigmatization toward Venezuelan immigrants in Peru within the context of the largest migratory movement in Latin America, driven by the political, economic, and humanitarian crisis in Venezuela. The study employs a qualitative approach, using semi-structured in-depth interviews with a diverse sample of 24 participants from major Peruvian cities, including Lima, Arequipa, Cusco, and Trujillo. These in-depth interviews provide insights into the complexity of perceptions toward Venezuelan migrants, ranging from stigmatizing views driven by associations with economic threats and criminality to more positive perceptions that acknowledge the migrants’ adaptability and economic contributions. The findings reveal that while negative stereotypes perpetuate social exclusion and pressures for cultural assimilation threaten the preservation of migrant identities, there are also narratives highlighting resilience and successful integration. The study emphasizes the importance of implementing intercultural education programs, promoting labor integration policies, and collaborating with the media to combat stigma. It concludes that addressing these challenges through a multidimensional, human-rights-based approach can foster greater social cohesion and better integration of migrants, benefiting both the migrant population and Peruvian society.

Infrastructure development is critical for sustaining Asia’s economic growth. Unfortunately, huge financing gaps—estimated by a recent Asian Development Bank study to be USD22.5 trillion—constrain the ability of most emerging Asian countries to fully realize the benefits of infrastructure development. For instance, over 70% of infrastructure investments in Asia are still funded by public resources, which pose acute financing challenges for many countries with limited budgets and fiscal constraints. This paper discusses some of the challenges associated with public financing of infrastructure projects in emerging Asian countries, before introducing some new options for alleviating their infrastructure investment needs. In particular, it proposes a new approach to infrastructure financing by utilizing the spillover effects of infrastructure investment, where additional revenues generated from such investment can be channeled back to investors as subsidy to increase the returns to their investment. The paper also argues the need for Asian countries to implement fiscal reforms and to develop a more balanced approach to financing, one that involves both the private and public sector. 

To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.