International student mobility is among the most visible human dimensions of contemporary globalization, yet globalization scholarship has read it largely through its macro-structures (flows, hubs, and policy competition) leaving undertheorized the social processes through which mobile students actually live it, above all in the non-Western host systems where educational globalization is now concentrated. This article advances a globalization-from-below argument: educational globalization is socially accomplished, or foreclosed, in the everyday micro-interactions through which mobile students build social capital, and the conditions of that accomplishment are structural and institutionally producible rather than matters of individual disposition. The argument is developed through semi-structured in-depth interviews with 23 international students from ten national backgrounds at a non-metropolitan public university in southern Thailand, analyzed through reflexive thematic analysis within a tripartite social capital framework complemented by Goffman's interactional and Bourdieu's dispositional micro-sociology. The analysis reveals a structural asymmetry at the heart of the sojourn: bonding capital is abundant yet can enclose; bridging capital with Thai peers is scarce yet qualitatively irreplaceable for cultural integration; and institutional linking capital is thin and largely informal. Digital technology, including AI-assisted translation, emerges as a distinctly contemporary globalization technology that lowers the threshold of cross-linguistic contact while imposing a ceiling on its depth. Because the conditions that enable bridging (dormitory co-residence, shared-goal activity, authentic English-medium delivery, and proactive institutional outreach) are institutionally producible, equitable participation in globalizing higher education is an institutional accomplishment rather than a by-product of mobility; the article closes with evidence-based design implications for the growing peripheries of regional education hubs.

Vehicle head and tail recognition systems play an important role in the monitoring and management of vehicles transporting mineral resources in smart mining systems. This work evaluates the accuracy of head and tail recognition using three versions of the You Only Look Once (YOLO) algorithm series: YOLOv5, YOLOv9, and YOLOv10. In the same experimental environment, by analyzing and comparing the recognition rate, parameter quantity, and recognition accuracy of the object detection model during the experimental process, the Sota model was selected before 2024. The experimental results showed that the recognition accuracies of YOLOv5, YOLOv9, and YOLOv10 were 73.93%, 91.80%, and 99.13%, respectively. Experiments show that under the same equipment conditions, the YOLOv model for identifying and detecting the head and tail of vehicles in the same dataset performs better, which has more advantages in improving the transportation of smart mines.

COVID 19 pandemic has changed learning delivery and services. Although many universities adapted to postpandemic needs, students view on preferences of learning modalities were not comprehensively examine, particularly, the Philippines. This study aims to understand students' preference of learning modalities after COVID 19 pandemic, given some changes. The study conducted a survey of university students taking Information Technology program. Results show that students generally prefer a learning modality setup that balances flexibility with more emphasis on in-person instruction to improve hybrid learning modality. Upperclass prefer face-to-face classes, particularly those in the upper years (3rd and 4th year) due to better interaction and focused learning. Hands-on courses like capstone and programming are seen as essential to be delivered in a faceto-face setup for practical experience. This study presents implications for improving the delivery of hybrid learning modality and research.

Artificial intelligence (AI) has demonstrated significant potential in transforming supply chain management (SCM) across industries, contributing to the economic, social, and environmental dimensions of sustainability. Despite this potential, research examining AI applications in sustainable SCM within small and medium enterprises (SMEs) remains scarce. In this light, this study conducts an exploratory literature review of 362 research publications between 2020 and 2025 using natural language processing (NLP) and data visualization techniques. Corpus distribution and frequency analyses indicate a rapidly growing research interest in the application of AI for sustainable supply chains for SMEs. Furthermore, Latent Dirichlet Allocation (LDA) topic modeling reveals a clear shift in the literature from general AI adoption toward specific applications and sustainability use cases, such as green energy transition, production waste reduction, green logistics, and circular economy. Notably, the findings position AI not merely as a tool, but as a key enabler of sustainability in SME supply chains. At the same time, the findings highlight persistent challenges faced by SMEs, especially in terms of organizational readiness, workforce capability, and technological uncertainty. Finally, the study offers implications at the theoretical, practical, and policy levels to support the integration of AI in sustainable SCM among SMEs.

Travelers are choosing eco-friendly accommodations, and the attention is drawn to sustainable tourism. Nevertheless, there is a gap between travelers' proenvironmental intentions on sustainable accommodation options using e-commerce platform, and it should be understood what influences decisions for eco-tourism. This study aims to explain ecommerce for sustainable accommodation adoption intention. The data used were from survey responses collected from travelers in the National Capital Region (NCR) of the Philippines to predict adoption intention. The performance of Support Vector Machine and Decision Tree in predicting sustainable accommodation adoption intention are found accurate and reliable. Awareness of consequences (AC), ascription of responsibility (AR), personal norms (PN) with attitude (AT), subjective norms (SN), perceived behavioral control (PBC) are key influencing factors, and all of these under the Theory of Planned Behavior (TPB). Finally, the Norm Activation Model (NAM) explains the role of trust (TR) and perceived value (PV) in the model. It shows knowledge of the intention behavior gap, and hence for the need of AI driven recommendations and policy incentives for the promotion of sustainable tourism. Overall, this study indicates the potential of e-commerce for sustainable accommodations behavior Intention using predictive models. Research and practical implications are presented.