While the move towards Industry 4.0 has motivated a re-evaluation of how a manufacturing organization should operate in light of the availability of a new generation of digital production equipment, the new emphasis is on human worker inclusion to provide decision making activities or physical actions (at decision nodes) within an otherwise automated process flow; termed by some authors as Industry 5.0 and seen as related to the earlier Japanese Society 5.0 concept (seeking to address wider social and environmental problems with the latest developments in digital system, artificial Intelligence and automation solutions). As motivated by the EU the Industry 5.0 paradigm can be seen as a movement to address infrastructural resilience, employee and environmental concerns in industrial settings. This is coupled with a greater awareness of environmental issues, especially those related to Carbon output at production and throughout manufactured products lifecycle. This paper proposes the concept of dynamic Life Cycle Assessment (LCA), enabled by the functionality possible with intelligent products. A particular focus of this paper is that of human in the loop assisted decision making for end-of-life disassembly of products and the role intelligent products can perform in achieving sustainable reuse of components and materials. It is concluded by this research that intelligent products must provide auditable data to support the achievement of net zero carbon and circular economy goals. The role of the human in moving towards net zero production, through the increased understanding and arbitration powers over information and decisions, is paramount; this opportunity is further enabled through the use of intelligent products.

As we progress towards Industry 5.0, technological advancements are converging; this movement is realised by the increasing collaboration between humans and intelligent digital platforms and further enabled by the interactive visualisation modes provided by Metaverse technology. This research examines the practical applications and limitations of Metaverse technology providing insights into the transformative possibilities it offers for the manufacturing sector. Specifically, the research was guided by the core objective to trace the evolution of Metaverse technology within manufacturing. This study provides a comprehensive and state-of-the-art analysis of the adoption and impact of Metaverse technologies in the manufacturing sector. While previous research has explored aspects of Industry 4.0 and digital transformation, this study specifically focuses on human-centric manufacturing (human-in-the-loop) applications of Metaverse technology, including augmented reality, virtual reality, digital twins, and cyber-physical robotic systems. Findings from the systematic literature review indicate that Metaverse technologies, primarily augmented reality and virtual reality, have evolved into powerful tools in manufacturing. They are widely adopted across sectors in the industry, transforming processes such as product design, quality control, and maintenance. Augmented reality and virtual reality offer intuitive ways to visualise data and interact with digital twins, bridging the gap between physical and virtual realms in manufacturing. A roadmap and scenarios for the introduction of Metaverse technology in manufacturing are provided with suggested adoption timespans. Furthermore, the systematic literature review identified barriers hindering the wider adoption of Metaverse technology in manufacturing.

The rise in EVs popularity, combined with reducing emissions and cutting costs, encouraged delivery companies to integrate them in their fleets. The fleet heterogeneity brings new challenges to the Capacitated Vehicle Routing Problem (CVRP). Driving range and different vehicles' capacity constraints must be considered. A cluster-first, route-second heuristic approach is proposed to maximise the number of parcels delivered. Clustering is achieved with two algorithms: a capacitated k-median algorithm that groups parcel drop-offs based on customer location and parcel weight; and a hierarchical constrained minimum weight matching clustering algorithm which considers EVs' range. This reduces the solution space in a meaningful way for the routing. The routing heuristic introduces a novel Monte–Carlo Tree Search enriched with a custom objective function, rollout policy and a tree pruning technique. Moreover, EVs are preferred overother vehicles when assigning parcels to vehicles. A Tabu Search step further optimises the solution. This two-step procedure allows problems with thousands of customers and hundreds of vehicles to be solved accomodating customised vehicle constraints. The solution quality is similar to other CVRP implementations when applied to classic VRP test-instances; and its quality is superior in real-world scenarios when constraints for EVs must be used.

Background: Theranostic approaches—the use of diagnostics for developing targeted therapies—are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments. Methods: We performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study ‘Understanding Society’. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response. Results: Our analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function. Conclusions: These findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases.