For businesses, the effective management of cold supply chains is critical to minimizing food losses and ensuring customer satisfaction. Identifying and prioritizing the obstacles that disrupt these processes is therefore a strategic necessity. However, existing literature largely addresses cold supply chain challenges in a fragmented manner, lacking systematic prioritization frameworks that account for the inherent uncertainty and subjective judgments present in real-world operations. To address this deficiency, this study proposes a structured decision framework based on the q-Rung Orthopair Fuzzy (q-ROF) Subjective Weighting Approach. This method effectively captures uncertainty and integrates expert evaluations to determine the relative importance of key cold chain barriers. Through an empirical application involving logistics managers, the framework ranks the identified obstacles to support operational and strategic decision-making. The findings reveal that Time Constraint is the most critical obstacle, directly impacting operational efficiency and customer satisfaction. In contrast, Temperature-Controlled Vehicle Cost is identified as a lower-priority factor in strategic resource allocation. These results offer a clear prioritization scheme that enables managers to focus resources on the most impactful areas, enhancing resilience and efficiency in cold chain operations. This study contributes a robust, uncertainty-aware methodology for barrier prioritization, providing actionable insights for supply chain practitioners and establishing a foundation for future research in cold chain management.
Steel surface defect detection is a critical task in intelligent manufacturing, where high accuracy and real-time performance are required for reliable quality inspection. However, existing deep learning-based approaches often rely on complex architectures, leading to increased computational burden and limited applicability in industrial environments with constrained resources. To address these challenges, a lightweight detection framework is developed to improve feature representation while maintaining computational efficiency. The proposed method integrates adaptive sampling with attention-guided feature refinement to enhance multi-scale feature extraction and contextual representation. In addition, an improved regression strategy is introduced to achieve more stable localization for irregular and low-contrast defects. The network structure is further optimized through lightweight design to reduce redundant parameters and support efficient inference. Experimental results on the Northeastern University surface defect detection (NEU-DET) dataset demonstrate that the proposed approach achieves improved detection accuracy with reduced model size and computational cost compared with baseline models. The results indicate that the method provides a practical solution for real-time industrial inspection, offering a balance between accuracy and efficiency in steel surface defect detection.
Open Access
News 
Journal Metrics
Journal Overview
Associated Journals