The term “Resilient Supply Chain 4.0” incorporates two research areas: Industry 4.0 and Supply Chain Resilience (SCRes). Industry 4.0 technologies include innovations such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), Augmented Reality (AR), Cloud Computing (CC), the Internet of Services (IoS), Big Data Analytics (BDA), Artificial Intelligence (AI), Digital Twins (DT), Blockchain (BC), Industrial Robotics (IR), and Additive Manufacturing (AM). Industry 4.0 technologies do not have a direct impact on SCRes, but on resilience elements such as flexibility, redundancy, visibility, agility, collaboration, robustness, and information sharing. This paper aims to investigate which of the Industry 4.0 technologies can help improve the Key Performance Indicators (KPIs) that are used for creating a Resilient Supply Chain 4.0. A non-systematic literature review has been conducted for the identification of (a) the most important constituent elements of SCRes, (b) the Industry 4.0 technologies that improve the SCRes elements, and (c) the KPIs that enhance SCRes. A systematic literature review has been conducted to identify which of the Industry 4.0 technologies have an impact on the KPIs that enhance SCRes. The findings of this work demonstrate that Industry 4.0 technologies can help improve the KPIs for a Resilient Supply Chain 4.0.

The pandemic and the various natural disasters related to climate change force Universities to operate remotely during certain periods of time. Synchronous and asynchronous Learning Management System (LMS) platforms at University provide a framework for distant operation. But it is crucial to maintain low students' attrition rates and ensure the quality of offered education at universities. This paper presents a survey of students' perceptions regarding their participation in online distance examinations in a Department of a Greek University. In this research, an online questionnaire is used in order to capture students' opinions about online distance examinations and to reveal information concern accessibility issues during the educational activities. The survey also recorded the students' opinion concerning the use of distance learning platforms, the communication with the professors and interactions between the students. According to the analysis of the questionnaires, students are willing to express their opinion on critical issues, such as the distance examinations and the problems that they face this period anonymously. To a satisfactory degree, they have the necessary equipment needed to participate in the distance educational process and they use the existing blended learning infrastructure.

Intensive Care Units (ICUs) generate a large amount of valuable data related to the health status of patients. In addition, ICUs can leverage other sources of big data, such as structured data, text, video, and images. In this work, a framework for the future ICU system is proposed, which is based on the Soft System methodology (SSM) and the use of big data technology. The framework and the related activity models ensure that the ICU can have its particularities and specialties, as well as its core services and functions. The application of the framework also implies that ICU can provide ongoing expertise and training to upgrade its staff, can improve interoperability with the National Health System, and ICU staff intercommunication and remote services.

Machine learning (ML) can enhance data-informed decision making, administrative capacity, and public service delivery, but its adoption extends beyond technical readiness. Public organizations operate within institutional environments shaped by legal obligations, transparency, organizational routines, and public trust. This paper develops a Machine Learning Adoption Readiness Framework through a conceptual and integrative research design. The framework comprises three interdependent dimensions: technological capacity, human and organizational capability, and governance maturity. Responsible ML adoption depends on their alignment across lifecycle, organizational, and governance requirements. Accordingly, readiness is conceptualized as a dynamic alignment condition supporting sustainable, accountable, and legitimate ML use in public administration.