Green initiatives have been widely introduced and have contributed to attaining sustainability and improving performance for supply chain management. However, only a few studies focus on green supply chain management (GSCM) practices in Vietnam. Hence, this work is the first study modeling the challenges in implementing green initiatives in the Vietnamese manufacturing supply chain. The authors aim to identify the fundamental challenges and evaluate the cross-interactions among them. The Interpretive Structural Model (ISM) method has been employed, based on experts’ perspectives, to clarify which factor is the most potent challenge. Consequently, seven major challenge clusters have been identified, and they were divided into nineteen sub-challenges. Meanwhile, the authors evaluated their interrelationships based on the hierarchical structure diagram and the Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis. It is observed that the “Financial Costs” elements group is the most difficult, followed by the lack of the Vietnamese government’s green regulation and the lack of senior managers’ support. The “Information” challenges cluster is considered as the middle bridge between the strong and weak elements. At the end of the diagram, two challenges are a lack of training courses about implementing GSCM and a lack of customer awareness and pressure about GSCM. Hence, these findings will become valuable suggestions for the top managers of Vietnamese manufacturers to make blueprint decisions.

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Sustainability concerns are rising as an interesting topic in both academia and industry. Many scholars revealed that green innovation is an excellent solution to enable organizations to achieve various benefits, such as enhancing their reputation and competitive advantages. Thus, this is the first study in Vietnam to consider the barriers to implementing green innovation. The research aims to identify the obstacles to green innovation practices in the Vietnamese manufacturing sector. The interpretive structural modeling (ISM) approach has been conducted to provide the interactions among the green innovation implementation barriers. The authors ground this study to bridge the theoretical and practical for green innovation practices in the Vietnam situation. Based on the experts’ perspectives, they proposed that Vietnamese manufacturers must deal with thirteen essential barriers to green innovation adoption. Further, six interaction levels and the MICMAC analysis clarified cross-relationships among challenges by evaluating the driving and dependence power. Indeed, the empirical results emphasized that financial capability constraints and lack of government support are the most decisive challenges. In contrast, market competition and uncertainty concern is the easiest obstacle to address by the Vietnamese manufacturers. Therefore, this study has provided some insightful contributions for the top managers and other scholars to consider.

Hand detection is a key step in the pre-processing stage of many computer vision tasks because human hands are involved in the activity. Some examples of such tasks are hand posture estimation, hand gesture recognition, human activity analysis, and other tasks such as these. Human hands have a wide range of motion and change their appearance in a lot of different ways. This makes it hard to identify some hands in a crowded place, and some hands can move in a lot of different ways. In this investigation, we provide a concise analysis of CNN-based object recognition algorithms, more specifically, the Yolov7 and Yolov7x models with 100 and 200 epochs. This study explores a vast array of object detectors, some of which are used to locate hand recognition applications. Further, we train and test our proposed method on the Oxford Hand Dataset with the Yolov7 and Yolov7x models. Important statistics, such as the quantity of GFLOPS, the mean average precision (mAP), and the detection time, are tracked and monitored via performance metrics. The results of our research indicate that Yolov7x with 200 epochs during the training stage is the most stable approach when compared to other methods. It achieved 84.7% precision, 79.9% recall, and 86.1% mAP when it was being trained. In addition, Yolov7x accomplished the highest possible average mAP score, which was 86.3%, during the testing stage.

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