This paper investigates the evolving clustering and historical progression of “Asian regionalisms” concerning their involvement in multilateral treaties deposited in the United Nations system. We employ criteria such as geographic proximity, historical connections, cultural affinities, and economic interdependencies to identify twenty-eight candidate countries from East Asia, Southeast Asia, South Asia, and Central Asia for this empirical testing. Using a social network analysis approach, we model the network of these twenty-eight Asian state actors alongside 600 major treaties from the United Nations system, identifying clusters among Asian states by assessing similarities in their treaty participation behavior. Specifically, we observe dynamic changes in these clusters across three key historical eras: Post-war reconstruction and transformation (1945–1968), Cold War tensions and global transformations (1969–1989), and post-Cold War era and globalization (1990–present). Employing the Louvain cluster detection algorithm, the results reveal the evolution in cluster numbers and changes in membership status throughout the world timeline. The results also identify the current situation of six distinct Asian clusters based on states’ inclinations to engage or abstain from multilateral treaties across six policy domains. These findings provide a foundation for further research on the trajectories of Asian regionalisms amidst evolving global dynamics and offer insights into potential alliances, cooperation, or conflicts within the region.

Arabic rhetoric has traditionally relied on ancient texts and human interpretation for teaching purposes. The study investigates ChatGPT’s ability to analyze and interpret Arabic rhetorical devices, specifically examining its capacity to handle cultural and contextual elements in rhetorical analysis. Drawing on institutional implementation frameworks and recent educational technology research, this study examines policy considerations for Arabic rhetoric education in an AI-driven environment, with a particular focus on sustainable digital infrastructure development and systematic reforms needed to support AI integration. The study employed the comparative approach to analyze eight rhetorical examples, including metaphors (“Zaid is a lion”), similes (“Someone is a sea”), and metonymy (“A person full of ash”), then compare ChatGPT’s interpretations with traditional explanations from classical Arabic rhetoric texts, particularly “Dala’il al-I’jaaz” by al-Jurjani. The results demonstrate that ChatGPT can provide basic interpretations of simple rhetorical devices, but it struggles with understanding cultural contexts and multiple layers of meaning inherent in Arabic rhetoric. The findings indicate that AI tools, despite their potential for modernizing rhetoric education, currently serve best as supplementary teaching aids rather than replacements for traditional interpretative methods in Arabic rhetoric instruction.

Agriculture is a determining factor regarding the development of the Romanian economy, noting its importance for population consumption and as a supplier of raw materials for the relaunch of other industries. Agricultural financing consists of credits granted to natural or legal persons for developing agricultural activities, expanding agricultural holdings, and commercializing agricultural production. The objective of this research is the statistical analysis of the determining factors in granting loans to Romanian farms. The study is based on the content analysis of the accounting reports of the 45 Romanian farms included in the research sample, based on which the profile of the farmer from the selected counties (Alba, Cluj, Mures, Sibiu, Dambovita and Prahova) is outlined. The obtained results highlight the fact that factors such as the requested amount (SUSO) are directly influenced by the worked area (TELU), by the turnover (CIAF), R = 0.6228, but also by the total value of the assets (TOTAL) R = 0.454. At the opposite pole, there is a weak correlation between SUSO and current liquidity (LICU), R = 0.2754, and the value of recorded expenses (CHEL), R = 0.3102. Implementing a credit policy that facilitates access to financing sources would support farms in modernization and development, increasing their competitiveness and general viability.