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.
Inclusive sustainability for vulnerable communities in the context of the digital twin transition necessitates a holistic approach that considers socio-economic disparities, cultural sensitivities, and accessibility barriers. By actively involving marginalized social groups in the design, implementation, and governance of digital twin initiatives, stakeholders can foster empowerment, resilience, and social cohesion. Strategies for inclusive sustainability must bridge the digital divide, enhance digital literacy, and promote participatory decision-making processes, while addressing systemic inequalities and promoting social justice. By prioritizing the needs and voices of vulnerable communities and embracing sustainability principles, societies can leverage digital twin technologies to create a more resilient, equitable, and sustainable future for all.
This paper aims to advance the initiation, expansion, and continuity of Energy Communities (ECs) by providing a framework and methodology tailored to enhance citizen and consumer engagement and facilitate the design of value-based propositions within ECs. Engaging EC members and stakeholders is crucial for establishing resilient ECs. Drawing from existing literature and research on Energy Communities, alongside established frameworks and best practices, this paper presents a framework for citizen engagement and value-based proposition design. Workshops and a social innovation template were developed based on these insights, forming the basis for exploratory dialogues with COMMUNITAS pilots. The methods and significant findings regarding consumer and citizen engagement and value-based design, derived from these pilot explorations, are discussed. Synthesizing the insights from the research and social explorations, implications for the COMMUNITAS project are outlined, particularly concerning the COMMUNITAS Knowledge Base and innovative models. Additionally, a framework for the value-based design of pilot activities and services, alongside citizen engagement, has been crafted. This framework aims to support Energy Communities in developing inclusive services and activities, positioning EC members at the forefront of energy markets. It serves as a foundational pillar for ongoing research within COMMUNITAS, evolving into a refined methodology over time.
In recent years, whether we are talking about industrial implementations or whether we are talking about products offered to the masses, there is an accelerated trend of the appearance of IoT devices, devices that collect information and, depending on their specifics, can make certain decisions in the process. This increase automatically leads to the generation of more generated data that must be sent for processing, analyzed, stored, a certain decision being made based on the result of the analysis. This process can be long, the duration strictly determined by the volume of data and the performance of the Cloud infrastructure. Fog and Edge computing has come to our aid with an innovative solution, acting as an additional layer between IoT and Cloud devices. This layer aims to reduce the response time by analyzing the data at the edge of the network, in this way it is no longer necessary to send all the information directly to the Cloud. Whether we are talking about smart cities, the health field or the industrial fields, the presence of IoT devices shows the usefulness of the need to implement Fog and Edge systems. Starting from this growth in the IoT field, the authors wish through this article to analyze the existing implementations that use Fog and Edge computing, the existing architectural levels, the analysis of the areas that present vulnerabilities, as well as the possible improvements that can be added to make the processes more efficient.
The Great Resignation phenomenon has underlined critical discontentment amongst the workforce worldwide, leading to a mass exodus from careers and industries. On the other hand, the advent of Industry 5.0 introduces an era of greater synergy between humans and technology, focusing on personalization, customization, and collaborative innovation. This research explores the potential link between these two prevailing trends, proposing that the human-centric nature of Industry 5.0 could potentially counter the Great Resignation trend. By cultivating a more fulfilling, adaptable, and collaborative work environment through the integration of advanced digital technologies and human creativity, Industry 5.0 may offer improved job satisfaction, work-life balance, and career progression opportunities. Therefore, it is hypothesised that Industry 5.0 could lead to a reversal of the Great Resignation, fostering a more satisfied and engaged workforce. This study employs a systematic literature review to substantiate this proposed link and its implications on future workplace dynamics and paved the road for further research.
In the Industry 4.0 scene, Artificial Intelligence (AI) is sought after as a new way of getting a competitive advantage from other market competitors. This technology can support not only in-line production status assessment processes, which enable a better control over the quality of the final product, but also to identify potential bottlenecks and other inefficiencies that can exist or occur in production processes. However, this technology has some obstacles that make its access difficult for businesses that do not have the necessary resources for implementing AI solutions, whether due to the intrinsic difficulty to handle such technologies, which require specialists (engineers, data scientists) that are not normally part of industrial human resources, or due to the integration and management of these technologies with already established processes and environments. To approach these technological accessibility challenges, some concepts are being applied, such as in the case of no code/low code solutions, i.e., the reduction or complete removal of programming requirements while using these technologies, and Machine Learning Operations (MLOps), where the integration and life cycle management of these solutions use the same approach as DevOps but applied and adapted to AI technologies. This paper presents an innovative, open-source and scalable approach towards AI pipeline creation, integration, and life cycle management in Industry 4.0 scenarios, in which these no code/low code and MLOps concepts are used, as well as a real-life application in the manufacturing industry.
Artificial Intelligence (AI) holds potential to ac-celerate various processes, alleviate human workload, enhance capabilities, and refine decision-making. In the context of cir-cular economy, AI offers various advantages, including defect identification, component classification, and end-of-life product management aligned with company strategies. Nevertheless, AI might have an impact on several Human Right (HR). This paper focuses on safeguarding HRs in the design and deployment of AI tools, particularly those advancing sustainability in manufacturing. A comprehensive Human Rights Impact Assessment (HRIA) is proposed, surpassing conventional methods to proactively mitigate risks aligned with AI regulations and ethical guidelines. The proposed HRIA was validated in three pilot cases that are implemented using an AI platform that works in tandem with Digital Twins to demonstrate circularity scenarios: de-and re-manufacturing of LIB packs in e-mobility, de-and re-manufacturing of consumer WEEE, and process industry in the petrochemical domain. These cases showcased the versatility of the proposed HRIA and its role in uncovering insights.
This paper presents the results of a qualitative transnational and cross-sectional study of the attitudes, fears, hopes and uses of AI in the framework of the KT4D HORIZON 2024 project (1). It contributes to the literature exploring social factors behind the discourse and practice surrounding AI. Focus group meetings with laypersons in Spain and Poland explored the impact of AI on healthcare, entertainment, education, employment and democracy. The expected changes in healthcare were perceived as most positive, whereas the impact on civil rights and democracy brought up the most fears regarding privacy, misinformation, manipulation and invasive surveillance. We conclude that digital literacy levels and trust influence heavily in the attitudes towards AI. We also suggest that media narratives should not be underestimated as a determining factor in building attitudes towards technology.
This paper provides strategic guidance through the development of a structured and systematic roadmap to support urban water supply utilities with the process of digital transformation. The roadmap design requirements are established and refined through a state-of-the-art literature review and empirical investigation utilizing semi-structured interviews with subject matter experts. The research identifies seven distinct roadmap phases to be completed in an agile, continuous, and iterative manner to better navigate the complex landscape of digital transformation. In particular, each of the respective roadmap phases is characterized by distinct objectives and equipped with practical tools enabling water supply utilities to exploit the potential of digital transformation according to their individual requirements and characteristics. Finally, the roadmap is verified and substantiated by performing face validation through semi-structured interviews.
In industrial production, Explainable Artificial Intelligence offers the opportunity to better understand AI models, not only to understand their decisions but also to be able to improve processes, which is possible even without the necessary experience. There are several widely used methods to implement this for Artificial Intelligence models, but no uniform standards and definitions. There are also still some hurdles on a technical and legal level as well as trust on the human side. Ultimately, the application of such techniques is particularly important in this area, but this is often not possible without the well-functioning and widespread use of Artificial Intelligence in manufacturing industry. However, it is a further step towards intelligent manufacturing and the acceptance of Artificial Intelligence in this area. The publication not only shows the latest developments, but also some use cases of Explainable Artificial Intelligence in manufacturing. Not only legal aspects but also ethical aspects are highlighted. In conclusion, it can be stated that Explainable Artificial Intelligence methods per se are well established, but their implementation and use in practice is lagging behind. A major point here in the future will be the explanation of generative artificial intelligence and user-centred explanations for non-domain experts. A standardised definition of explainability and standards for evaluating the individual methodologies must also be created.
Background: Procurement processes are important to streamline efficiency and innovation in Hospitals. However, in the face of an immense portfolio of products and technologies available, an efficient hospital will need to make an effort to digitalize the procurement process, based on scientific and technical evidence. For instance, surgical wound closure devices have progressed from sutures to contemporary innovations like cyanoacrylate-based adhesives and mesh integration. It's necessary to understand the advantages or disadvantages of these new devices systematically. Objective: To support our proposal for a digital, evidence-based procurement process platform we aim to conduct a scoping review, using as an example the use of 2-octyl cyanoacrylate plus polymer mesh tape in surgical incision closure, focusing on clinical efficacy, cost, and patient satisfaction. Methods: Based on the Design Science Research Methodology (DSRM), this study focuses on the first and second stages, the problem identification, and objectives for a solution. For that purpose, we conducted a scoping review combined with observations, following the Joanna Briggs Institute manual and PRISMA guidelines, as well as Arksey, O'Malley, and Levac et al.'s steps. Results: We selected 33 articles via Rayyan, evaluating 2-octyl cyanoacrylate (2-OC) with and without polymer mesh tape against various methods. Results show promising outcomes in clinical efficacy, cost-effectiveness and quality of life. This review highlights process complexity and inconsistent measuring parameters. Stakeholder meetings identified the need for a digital, evidence-based procurement process. Conclusions: Digitalizing healthcare procurement processes enhances efficiency by integrating evidence-based approaches informed by science. Recognizing the importance of systematically evaluating medical devices, we're crafting a digital procurement process that incorporates rigorous assessments based on HTA. In the next stage, we will design a platform that delivers a seamless experience, empowering stakeholders to make informed decisions that ultimately enhance patient care and healthcare delivery.
Corporate Entrepreneurs have become an important resource for established companies to drive entrepreneurial behavior for new types of innovation and cultural change. Identifying and hiring employees with tailored competences and behaviors of a Corporate Entrepreneur is critical given that most corporates struggle to find the knowledge and capabilities needed to develop new types of ventures. The knowledge of CVs is described as relevant for hiring and developing employees but has not yet been sufficiently considered in the context of CE. Therefore, this study investigates past career experiences by leveraging optimal matching analysis, we examine a dataset comprising 50 sequences extracted from LinkedIn profiles, representing various career transitions over a 10-year period. Our findings reveal a dominant trajectory within the corporate sector, with limited transitions to other categories such as startups or consulting roles. With this, our research contributes to a deeper understanding of the career experiences of corporate entrepreneurs and underscores the need for future studies to explore the holistic factors shaping their professional trajectories.
This paper explores the disruptive potential of blockchain technology in advancing sustainable energy practices within communities through the implementation of a novel oper-ational stack layer 2 (L2) rollup. This blockchain solution is de-signed to enhance transparency, ensure genuine decentralization, and facilitate interoperability in energy trading. By integrating an L2 rollup on Ethereum, the system addresses critical scalability issues inherent in traditional decentralized blockchain models, thereby reducing transaction costs and increasing throughput. The primary focus is on how such a technological framework can empower communities to manage their energy resources more efficiently and sustainably. The paper also discusses the technical architecture of the L2 rollup and the potential barriers to its adoption. Through this analysis, we argue that blockchain technology, particularly through enhancements like L2 rollups, is crucial for sustainable communities focused on renewable energy trading with no central third-party control. This paper also overviews different market structures, particularly the uniform bidding market structure, chosen for its simplicity and effectiveness in aligning with the decentralized ethos of blockchain technology. This market model ensures that all participants are treated equitably, paying the same market-clearing price that reflects the true supply and demand of the energy market.
This study aims to investigate the information systems used in small business networks with systemic and personal motives. More precisely, the study focuses on the natural products sector in Finland as a case example of a developing field with a high number of small companies. In doing this the information exchange and the information systems in the field are identified and their utilization analyzed. As a result, the study provides insights into the nature of the networks, how knowledge and information exchange in and between them is carried out, and what factors should be considered when managing those.
This paper explores the role of Resilient Learning in enhancing knowledge, competence, and capability building for critical infrastructure resilience and fostering proactive learning methods for effective European critical infrastructure protection. Resilient Learning, the ability to absorb and adapt to unexpected events, such as early warnings, enables the development of effective response-mitigation mechanisms. The research setting for Resilient Learning is presented through a case study relevant to the ongoing innovation project “European Knowledge Hub and Policy Testbed for Critical Infrastructure Protection (EU-CIP}”, providing a foundation for investigating the concept from various perspectives. The study examines the advancement of Resilient Learning in collective knowledge-competence-capability building, identifying resilient and elastic learning paths, and recognising characteristics of unawareness-related learning. This involves transitioning from learning about expected phenomena to learning about unexpected ones, demonstrating the ability to adapt to the unknown. The study contributes to the advancement of Resilient Learning by providing a detailed description and exploring the accommodation settings in collective-federated learning processes. These processes, interconnected to regional, national, European, and global critical infrastructure systems, represent continua of learning paths that enhance resilience. The study concludes by emphasising the significance of Resilient Learning in fostering proactive response mechanisms and enhancing resilience in European critical infrastructure systems Initial results for applying the study in the maritime sector, especially in the small ports' critical information infrastructures is presented.
In contemporary manufacturing systems, the efficiency of the production line is crucial for maintaining competitiveness in the current market. Therefore, minimizing re-machining operations becomes imperative to optimize production, reduce costs, and utilize resources more effectively. This necessity is particularly apparent in machining processes involving sizable components that require numerous hours, and at times, even days to accomplish. This study employs two Artificial Neural Networks with one layer and different numbers of neurons to determine the required material removal quantities for the production of cast iron columns with different lengths. The algorithm was tested on a 3-axis CNC machine in a real industrial scenario in the company PAMA S.p.A., a member of the European AIDEAS project consortium. The goodness of the solution has been demonstrated by the high score value in the prediction of the removal parameters (score = 0.998) and confirmed by the experience of the production manager of the company.
