The Production Logistics system is generally a large-scale complex system with various operational phases and management levels that must integrate. In the specific context of soft and deformable food products, the core of AGILEHAND European project, this complexity increases further due to challenges related to the handling and movement of such items. Efficient coordination of production and logistics phases becomes crucial to ensure product quality, prevent losses, and optimize the entire process. In this article, focus will be placed on a data-driven framework for the automated generation of simulation models, serving as the foundation for digital twins in intelligent factories within the previously mentioned sector. The proposed framework represents a multi-layered data-driven system designed for real-time/near-real-time simulation, planning and synchronization of production and logistics systems during line reconfiguration. The digital model forms the basis for a digital twin with simulation and optimization capabilities, designed to facilitate decision-making at various management levels in the production and logistics process and control activities such as changes, maintenance, quality and safety. Exploiting information provided by the Enterprise Traceability system, the digital twin aims to establish a real-time/near-real-time information flow. This flow enables accurate capturing of dynamics occurring in the physical layer and effective assessment of their negative effects on the overall operational state of the system in the digital layer. In this context, the use of the digital twin is intended to simplify and expedite the reconfiguration of production and logistics systems. This is achieved through the early detection of system design or process sequence through cross-sectional simulation.
Colorectal cancer (CRC) represents a significant global health challenge, ranking among the leading causes of cancer-related morbidity and mortality worldwide. However, despite its high incidence and mortality, CRC is considered a most preventable cancer through a healthy lifestyle adoption that may impede the development of colorectal adenomas and their transition into malignant states. In this regard, although the notions of healthy living and behavioral pathways are well-known, population adherence and awareness in relation to cancer prevention are lacking. Moreover, the evaluation of their impact on biological CRC-related processes and risk via quantifiable biomarkers remains under research. In this regard, this paper presents the design and implementation strategy of a multi-center clinical study investigating the potential of a risk assessment and behavioral change framework for CRC prevention, delivered via a digital solution. Particularly, the DIOPTRA mobile app will integrate accessible risk assessment and personalized behavioral recommendations to empower users in shifting towards healthier lifestyles. Key features of the app will include behavioral questionnaires, risk assessment, personalized suggestions, health literacy, and a diary function to track behaviors and mild symptoms. Upon study enrollment, participants will receive personalized assessment and recommendations, before a follow-up that will investigate adherence, effects, and quality of life. Moreover, a biomarker-based evaluation will be integrated, based on an under-development minimally-invasive screening blood test. Overall, this study aims to highlight the importance of mHealth solutions for CRC prevention on a population level, as well as to establish clinical relevance by seeking to link behavioral change with novel CRC biomarkers.
The industrial evolution and emerging global challenges in the 21st century demand innovative strategies from firms to remain competitive. Scholars increasingly explore the phenomenon of hybridization within business models (BMs) to address these multifaceted challenges. However, the dynamics and characteristics of hybridization within BM configurations and patterns remain largely unexplored. The aim of this study is to analyze BM activities of young ventures and entrepreneurial projects, and to investigate the impact of hybrid BM components on venture performance, providing valuable insights into the complex relationship between multiple hybrid BM dimensions. Utilizing a comprehensive dataset rooted in activity-based text data, we employ text-mining and qualitative clustering methods to identify BM design variables across multiple BM dimensions within a morphological framework. Based on the developed framework, the BM design variables are then calibrated into conditions for Qualitative Comparative Analysis (QCA) to examine the impact of the hybridization of BM components on venture performance. The investigation yielded 59 distinct BM design variables across eight dimensions within three BM components. We identified three consistent BM configurations through QCA crucial for venture performance. These configurations highlight the significance of aligning various BM components and emphasize how customer and product orientation within business strategy can be used to gain competitive advantages in complex markets.
