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In the context of digitalizing the supply chain of different sectors, the blockchain technology (BT) is emerged and becomes one of the most interesting and debated research topics. A few years ago, this technology was implemented for the first time in the financial sector, which is currently extended to be used in many other areas, mainly the health-care sector. The success of this technology stems from its ability to enhance the performance, security, consistency of sharing medical data within the whole system and the analysis of medical records. Technically, the BT is defined as a decentralized digital ledger that records transactions between the stakeholders in a supply chain. Thus, they could gain better control and find severe mistakes and the unsafe ones in the medical field. The purpose of this paper is to address a preliminary study of the BT adoption in a real pharmaceutical supply chain (PSC) of the Tunisian case.

Indeed, the authors propose an interpretive structural modeling (ISM) combined with the cross-impact matrix multiplication applied to classification (MICMAC) analysis. This methodology is known as an effective one used to identify the criteria that influence the implementation of the BT, analyze the relationships between them and delighting the most impactful ones.

The readiness criteria for the adoption of the BT for the considered system are identified which are 10 ones and the structural relationship between them is uncovered through many interviews with experts. Hence, the found results define the most crucial criteria that should be valorized amongst the other criteria.

The originality of this study stems from its theoretical and practical relevance regarding the potential of the pharmaceutical system and the importance of the integration of new technologies as the BT. The ISM-MICMAC approach seems to be very performant for such preliminary study of the BT adoption in the Tunisian pharmaceutical system.

Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.