Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation.

Industrial equipment/machinery is an important element of manufacturing. They are used for producing objects that people need for everyday use. Therefore, there is a challenge to adopt effective maintenance strategies to keep them well-functioning and well-maintained in production lines. This will save energy and materials and contribute genuinely to the circular economy and creating value. Remanufacturing or refurbishment is one of the strategies to extend life of such industrial equipment. The paper presents an initial framework of cost estimation model based on combination of activity-based costing (ABC) and human expertise to assist the decision-making on best life extension strategy (e.g. remanufacturing, refurbishment, repair) for industrial equipment. Firstly, ABC cost model is developed to calculate cost of life extension strategy to be used as a benchmark strategy. Next, expert opinions are employed to modify data of benchmark strategy, which is then used to estimate costs of other life extension strategies. The developed cost model has been implemented in VBA-based Excel® platform. A case study with application examples has been used to demonstrate the results of the initial cost model developed and its applicability in estimating and analysing cost of applying life extension strategy for industrial equipment. Finally, conclusions on the developed cost model have been reported.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of modern industrial systems. Industry 4.0 assumes a higher level of intelligence, and thus autonomy of the systems and subsystems, and a larger focus on the analysis of gathered data for further utilization. The Virtual Factory Open Operating System (vf-OS) project is intended to respond to some of these key challenges, in particular for the smart factory application domain. Complementarily, data and knowledge storage and processing are also in the scope of vf-OS. This article introduces the semantic management component of vf-OS, which aims to analyze the interrelations among stored entities, as well as to define the closeness among them to generate meaningful suggestions, which can be later used by other subsystems or operators in a user-friendly way. The semantic managing system makes use of non relational approaches, namely a graph database, which enables data to be represented as graphs for further semantic querying. The developed prototype and an illustrative application case are also presented.

In the last decades, the importance of social media has increased extremely with the creation of new communication channels and even changing the way people are communicating. These trends came along with the disadvantage of allowing a new scenario where messages containing valuable data about critical threats like terrorism and criminal activity are ignored, due to the sheer inability to process – much less analyze – the vast amount of available data. Terrorism has a very real and direct impact on basic human rights of victims, such as the right to life, liberty and physical integrity, often with devastating consequences.

The authors of the article examined the modern challenges involved in developing an access control system for smart factories, focusing on the creation of an integrated solution for monitoring and managing the information security of the Internet of Things (loT) through the integration of diverse tools and methods within a single system. In this paper, the information system is examined through the lens of system analysis, focusing on the interactions between the subjects and objects within the system. This perspective allows for an accurate assessment of the current state of obj ects, taking into account the system's architecture and its vulnerabilities, as well as the evolution of the system state over time. The approach proposed incorporates modern tools and software solutions, including intrusion detection systems (IDS), fuzzy testing, Software Bill of Materials (SBOM), and various machine learning (ML) techniques.