The circular economy (CE) has gained increasing attention to improve resource efficiency and reducing environmental impacts. While existing research has largely focused on macro-level indicators and organizational strategies, the role of individual users and consumers in contributing to circular practices remains insufficiently quantified. In parallel, large volumes of unstructured textual data capturing user interactions, problem-solving behaviors, and experiential knowledge related to CE practices are increasingly available across digital platforms, yet remain underutilized for systematic assessment. This paper proposes a natural language processing (NLP)-based framework for quantifying user-level contributions to the CE. The framework integrates semantic inference and behavioral modeling within a layered system architecture to transform heterogeneous and unstructured textual interactions into a unified Circular Contribution Score (CCS). Semantic alignment with circular practices is inferred through interpretable classification subtasks capturing issue relevance, resolution status, and solution type, while user engagement behavior is quantified using interaction metadata. These dimensions are aggregated and normalized to calculate user-level scores reflecting both the quality and intensity of contribution. The proposed approach provides an interpretable and adaptable mechanism for assessing individual contributions to CE practices, enabling comparative analysis across users and platforms. By bridging unstructured discourse analysis and quantitative measurement, this work contributes a scalable methodology for monitoring and evaluating user participation in companies’ CE practices.

The transformation of industries towards low-carbon energy depends on the level of renewable energy integration and access to green energy resources. The optimisation of energy demand and enhancement of resource efficiency through the production, delivery, and usage of clean energy is vital for the transition to a low-carbon, secure, and affordable industry. In fish processing industries, the efficient coordination of energy flows at the site-level can unlock energy potential and stimulate the development of carbon-free economies through the deployment of renewables and storage. As the integration of renewable energy into the network is no longer a technical issue, it has become possible to advance the energy sustainability agenda and enable a revenue stream market while ensuring reliable, flexible, and secure energy networks.

This paper presents an optimisation-based strategy for controlling energy flows in port microgrids, using a case study from the Milford Haven port in the UK. We focus on deploying solar PV and battery storage to reduce costs and emissions in a fish processing facility by implementing mixed linear programming techniques, demonstrating applicability to port-level operations. Our findings demonstrate that these energy control techniques can reduce energy costs by 33% to 55%, while also enabling access to and integration with cleaner energy sources.

As a core component of spring‑energized seals, the double‑layer annular helical spring directly determines the reliability and adaptability of sealing systems in advanced industrial equipment. To address the current research gap—where studies rely heavily on finite element simulations without a solid theoretical foundation—this paper introduces the concepts of overall stiffness and cross‑section stiffness to characterize macro‑structural deformation and local contact behaviour, respectively, and proposes two novel mechanical models. An analytical model for overall stiffness is developed based on first‑order shear deformation theory (FSDT) and the unified adaptive approach (UAA). For cross‑section stiffness, a unified description covering both elastic and elastic‑plastic stages is constructed using a symmetric contact model and Hertz contact theory. The load‑displacement curves of the spring are obtained through theoretical calculations, finite element simulations, and experimental tests to validate the models. The results show that the proposed models agree well with simulation and experimental data under typical operating conditions, accurately capturing the nonlinear mechanical behaviour of the double‑layer annular helical spring. By providing a rigorous theoretical foundation and efficient analytical tools for parametric design, this study contributes to the development of high‑performance, long‑life sealing components—an essential element for intelligent manufacturing systems and Industry 5.0, where resilient, sustainable, and human‑centric production relies on the predictable performance of critical mechanical components.

As the servitisation of industrial products has become more popular in recent years, traditional business models in robotics have also been evolving to better adapt to and exploit Industry 4.0 technologies. The two main Robot-as-a-Service (RaaS) business models found in the industry are robot leasing and cloud-based Industrial Internet of Things (IIoT) platforms. This paper reviews and explores current uses cases and examples of both hardware and software RaaS found in the industry from robot manufacturers and system integrators. The main contributions of this work are the two case studies that are presented, i.e. hardware-based and software-based RaaS.

In this paper, a multi-stage workflow based on a Geographic Information System (GIS) tool is presented to identify candidate helicopter landing zone (HLZ) points in the Republic of Ireland for use in helicopter emergency operations. Digital elevation data are used to evaluate terrain slope and steepness to generate an initial set of candidates landing points. Different classes of landing obstacles, including both natural and man-made features, are then filtered step by step from these candidate points, resulting in a set of feasible HLZ locations. The resulting HLZ points are subsequently clustered according to the land characteristics of their surrounding area in order to support faster and more practical landing-site selection. This work contributes to the advancement of geospatial decision-support methodologies for aeromedical emergency operations by proposing a multi-stage GIS-based workflow for the identification of suitable HLZs.