Bojana Bajic

In recent years, researchers and practitioners have focused on Industry 4.0, emphasizing the role of cyber-physical systems (CPSs) in manufacturing. However, the operationalization of Industry 4.0 has presented many implementation challenges caused by the inability of available technologies to meet industry needs effectively. Furthermore, Industry 4.0 has been criticized for the absence of focus on the human component in CPSs impacting the concept of sustainability in the long run. Responding to this critique and building on the foundation of the Industry 5.0 concept, this article proposes a holistic methodology empowered by human expert knowledge for human-cyber-physical system (HCPS) implementation. The proposed novel HCPS methodology represents a more sustainable solution for companies that consists of five phases to promote the integration of human expert knowledge and cyber and physical parts empowered by big data analytics for real-time anomaly detection. Specifically, real-time anomaly detection is enabled by industrial edge computing for big data optimization, data processing, and the industrial Internet of Things (IIoTs) real-time product quality control. Finally, we implement the developed HCPS solution in a case study from the process industry, where automated system decision-making is achieved. The results obtained indicate that an HCPS, as a strategy for companies, must augment human capabilities and require human involvement in final decision-making, foster meaningful human impact, and create new employment opportunities.

In the last decade, researchers have focused on digital technologies within Industry 4.0. However, it seems the Industry 4.0 hype did not fulfil industry expectations due to many implementation challenges. Today, Industry 5.0 proposes a human-centric approach to implement digital sustainable technologies for smart quality improvement. One important aspect of digital sustainability is reducing the energy consumption of digital technologies. This can be achieved through a variety of means, such as optimizing energy efficiency, and data centres power consumption. Complementing and extending features of Industry 4.0, this research develops a conceptual model to promote Industry 5.0. The aim of the model is to optimize data without losing significant information contained in big data. The model is empowered by edge computing, as the Industry 5.0 enabler, which provides timely, meaningful insights into the system, and the achievement of real-time decision-making. In this way, we aim to optimize data storage and create conditions for further power and processing resource rationalization. Additionally, the proposed model contributes to Industry 5.0 from a social aspect by considering the knowledge, not only of experienced engineers, but also of workers who work on machines. Finally, the industrial application was done through a proof-of-concept using manufacturing data from the process industry, where the amount of data was reduced by 99.73% without losing significant information contained in big data.

The ecological state of the Danube River, as the world’s most international river basin, will always be the focus of scientists in the field of ecology and environmental engineering. The concentration of orthophosphate anions in the river is one of the main indicators of the ecological state, i.e., water quality and level of eutrophication. The sedentary nature and ability to survive in river sections, combined with the presence of high levels of orthophosphate anions, make macrophytes an appropriate biological parameter for in situ prediction of in-river monitoring processes. However, a preliminary literature review identified a lack of comprehensive analysis that can enable the prediction of the ecological state of rivers using biological parameters as the input to machine learning (ML) techniques. This work focuses on comparing eight state-of-the-art ML classification models developed for this task. The data were collected at 68 sampling sites on both river sides. The predictive models use macrophyte presence scores as input variables, and classes of the ecological state of the Danube River based on orthophosphate anions, converted into a binary scale, as outputs. The results of the predictive model comparisons show that support vector machines and tree-based models provided the best prediction capabilities. They are also a low-cost and sustainable solution to assess the ecological state of the rivers.

During the last decade, we have witnessed steady movement of industry and academia toward Industry 4.0. Industry 4.0 is a concept aimed at achieving the integration of physical and cybernetic parts of the manufacturing process via networks and driven by Industry 4.0 technology categories used for the prediction, control, maintenance, and integration of manufacturing processes (i.e., cyberphysical systems, Internet of Things, big data analytics, cloud computing, fog and edge computing, augmented and virtual reality, robotics, cybersecurity, semantic web technologies, and additive manufacturing). The research on implementing Industry 4.0 is lagging behind the degree of attention Industry 4.0 attracts. Moreover, contrary to the exponential growth that Industry 4.0 research shows, the implementation of Industry 4.0 in practice is less often covered in the relevant literature. The challenges of implementing Industry 4.0 are even less frequently addressed. Industry 4.0 implementation challenges can be either managerial or technological. In our recent paper, we analyzed managerial implementation challenges. This article, as a continuation of our previous work, identifies, codes, and defines the technological Industry 4.0 implementation challenges. It further analyzes hindering relationships between Industry 4.0 technologies and identifies main challenge themes important for system designers. Finally, this article derives opportunities in the form of recommendations for overcoming current and future technological implementation challenges.