Khwansuwan, Poon
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Preferred name
Khwansuwan, Poon
Main Affiliation
Email
poon.kh@kmitl.ac.th
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Item type:Publication, Strategic Approach of Reverse Logistics Management for Recyclable Waste and Transportation: A Systematic Review(2026-01-01); ; ; Strategic reverse logistics management is a key driver of sustainability in supply chains, where challenges in recyclable waste must be aligned with transportation systems to achieve optimal outcomes. A systematic review using the PRISMA methodology was conducted in December 2024 by searching Scopus, Google Scholar, and Thai Journals Online to examine the global research landscape and the strategic approaches applied in reverse logistics for recyclable waste and transportation. Analysis of 32 publications shows a steady rise in research, with most studies in Asia and dispersed across multiple journals, reflecting the field’s multidisciplinary nature. Four strategic approaches were identified. Model-driven approaches demonstrate strong capability through mathematical, computational, conceptual, and hybrid models, achieving reductions of 44% in climate impacts and 34% in costs. Technology-driven approaches contribute innovations to enhance battery transport safety. Exploratory approaches reveal contextual policy gaps and financial limitations. Hybrid approaches can improve efficiency and reduce CO<inf>2</inf> emissions. The future development of hybrid approaches still offers substantial room for broader application and deeper integration. This review supports the development of more effective systems, policies, and future research. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning-Based Heritage Building Assessment with Spatial Context(2025-01-01); ; ; Sirikitsathian, PhatthananRapid urbanization threatens architectural heritage in developing regions, where limited conservation experts cannot assess thousands of potentially valuable buildings before irreversible modifications occur. This paper presents an automated screening system for heritage building identification using deep learning and spatial analysis. The proposed framework employs a dual-stream architecture combining YOLOv8 object detectionwith SegFormer semantic segmentation to extract architectural features from building facade photographs. These visual features are integrated with Geographic Information System (GIS) data to incorporate spatial context, recognizing that heritage buildings often cluster in historically significant areas. A hybrid weighting mechanism balances data-driven feature importance (80%) with expert architectural knowledge (20%) to ensure cultural sensitivity. Experimental evaluation on 1,500 buildings in Roi Et Province, northeastern Thailand, demonstrates the system's effectiveness, achieving 87.6% classification accuracy while processing each building in approximately one second. In corporating spatial context improved performance by 6.4% over visual features alone. The transformer-based architecture proved particularly effective at identifying characteristic features such as paired windows and traditional wall patterns that distinguish heritage structures. This work provides a practical tool for large scale preliminary heritage assessment, enabling conservation authorities to efficiently allocate limited expert resources to high priority buildings while maintaining classification reliability suitable for initial screening purposes.