This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieved a precision of 0.932, an mAP@0.5 of 0.938, and an F1-score of 0.914, with an average inference time of ~470 ms on a Raspberry Pi 500, confirming its feasibility for real-time edge applications. The proposed system aims to replace physical jigs used for the dimensional verification of extruded polyamide tubes in the automotive sector. The YOLOv8 Nano and YOLOv8 Small models were trained on a Graphics Processing Unit (GPU) workstation and subsequently tested on a Central Processing Unit (CPU)-only Raspberry Pi 500 to evaluate their performance in constrained environments. The experimental results show that the Small model achieved higher accuracy (a precision of 0.951 and an mAP@0.5 of 0.941) but required a significantly longer inference time (~1315 ms), while the Nano model achieved faster execution (~470 ms) with stable metrics (precision of 0.932 and mAP@0.5 of 0.938), therefore making it more suitable for real-time applications. The system was validated using authentic images in an industrial setting, confirming its feasibility for edge artificial intelligence (AI) scenarios. These findings reinforce the feasibility of embedded AI in smart manufacturing, demonstrating that compact models can deliver reliable performance without requiring high-end computing infrastructure.

The new processes for using digital technologies, are requiring manufacturing companies to adapt to these new scenarios, causing a digital transformation. Although the theme Digital transformation is new and current in manufacturing, the way it is being implemented by manufacturing companies is in design and study. Each company adopts or plans its strategy in relation to the digital platform and digital ecosystem. Two digital technologies are widely cited when discussing digital transformation, they are the digital platform and the digital ecosystem. The purpose of this article is to analyze the digital transformation in manufacturing companies from the perspective of digital platforms and ecosystems. In order to answer the research questions, the strategy of this research project was the study of multiple cases with a longitudinal perspective and an exploratory approach. The interview was the main source of data collection. The five platforms studied presented different views of how digital transformation can occur in manufacturing companies, from the simple transaction of matching supplier and consumer to collaborative work with the different stakeholders of the companies.

The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, and bay leaf—using advanced computer-aided detection within the You Only Look Once (YOLO) framework. Employing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the study meticulously covers data understanding, preparation, modeling, evaluation, and deployment phases. The dataset, consisting of images from diverse devices and annotated with bounding boxes, was instrumental in the training process. The model’s performance was evaluated using the mean average precision at a 50% intersection over union (mAP50), a metric that combines precision and recall. The results demonstrated that the model achieved a precision of 0.7 or higher for each herb, though recall values indicated potential over-detection, suggesting the need for database expansion and methodological enhancements. This research underscores the innovative potential of deep learning in aromatic plant identification and addresses both the challenges and advantages of this technique. The findings significantly advance the integration of artificial intelligence in agriculture, promoting greater efficiency and accuracy in plant identification.

In complicated and fast-moving markets, where growth and wealth creation can occur unpredictability reigns. The ways in which value is created, captured, and offered are changing profoundly, and one of those responsible for this change is digital transformation. New innovative business models are born, and it becomes necessary to rethink the models when it comes to the digital age. The objective of this work is to conceptualize the strategic changes in the business models of the companies to remain competitive of the era of the digital transformation. In order to collect the information necessary for the analysis, we chose to use exploratory research of a qualitative nature. It is justified to adopt the exploratory research, since there are few studies focused on the theme of business model strategy in the era of digital transformation. In our research, the companies interviewed highlight the digital transformation to improve and integrate with more automation all the lines of business segmentation.

Correctly identifying and classifying food is decisive in food safety. The food sector is constantly evolving, and one of the technologies that stands out is augmented reality (AR). During practical studies at Companhia de Entreposto e Armazéns Gerais de São Paulo (CEAGESP), responsible for the largest food storage in South America, difficulties were identified in classifying aromatic herbs due to the large number of species. The project aimed to create an innovative AR application called ARomaticLens to solve the challenges associated with identifying and classifying aromatic herbs using the design science research (DSR) methodology. The research was divided into five stages according to the DSR methodology, from surveying the problem situation at CEAGESP to validating the application through practical tests and an experience questionnaire carried out by CEAGESP specialists. The result of the study presented 100% accuracy in identifying the 18 types of aromatic herbs studied when associated with the application’s local database without the use of an Internet connection, in addition to a score of 8 on a scale of 0 to 10 in terms of the usability of the interface as rated by users. The advantage of the applied method is that the app can be used offline.

The term social enterprise has gained notoriety since 2006, when Muhammed Yunus won the Nobel Peace Prize for his work with Grameen Bank, resulting in a new business concept and the proposal of a new capitalist model. Social enterprises are focused on solving social, economic and environmental problems that have long plagued humanity, such as hunger, disease, pollution and homelessness. Due to the growing literature on social enterprises and the fragmentation of theories and concepts surrounding this term, this study investigates and shows that research on social enterprises develops over time. The research consists of a bibliometric analysis based on citation data from articles, books, and other materials available in the Thomson Reuters Web of Science database. This study analyzed 1063 articles on social enterprises dating from 2008 to 2018, noting that the number of publications on social enterprises changes over time.