Zero Emissions Resource Organisation

This paper analyses how small and medium-sized enterprises (SME) in the emerging economies are adopting Industry 4.0 with specific reference to cloud-to-edge transformation and its potential impact on productivity and employment. Although the use of new digital technologies has become common among large ventures, SMEs are usually constrained due to cost, capacity, infrastructures, and data management. The article builds a conceptual cloud-to-edge Industry 4.0 system that combines IoT-enabled systems, edge and fog nodes, and scalable cloud servers to aid in real-time analytics, automation, and data-driven decision-making under resource-limited conditions. The survey evidence, secondary datasets, and example case studies of manufacturing, service, and agro-industrial SMEs are mixed with the survey evidence in the study using the mixed-method research design. The effects of productivity are measured in terms of operation efficiency, decreased downtime, quality, and responsiveness of the supply chain, whereas the effects of employment are measured regarding creation of jobs, change in tasks, intensity of skill, and productivity of labour. The results show that the cloud-to-edge architectures contribute to much better visibility of the processes and predictive maintenance as well as latency reduction in comparison to cloud-only systems, which translate to productivity gains. The impact on employment is less obvious: there is a tendency to replace routine tasks with robots, but there are new jobs in the field of supervising systems, data processing and management, and high demand for reskilling. The paper identifies policy, infrastructure, and capability-building issues that should be in place to result in inclusive digital transformation.

The pursuit of high-energy lithium-ion batteries (LIBs) has driven the adoption of Ni-rich layered oxides such as NMC811, but their reactivity with conventional ethylene carbonate (EC)-based electrolytes leads to interfacial instability and performance loss. Here, we systematically investigate how electrolyte composition and additives influence the formation and stability of the solid electrolyte interphase (SEI) in graphite||single-crystal NMC811 cells. Using commercial-grade pouch cells under a consistent formation protocol, we combine operando acoustic emission (AE) and electrochemical atomic force microscopy (EC-AFM) with complementary electrochemical and structural characterisation to probe SEI formation in real time. We first assess common additives in EC-containing electrolytes, then progressively reduce EC content, and finally introduce SEI-forming additives into EC-free electrolytes. While EC promotes SEI growth on graphite, it also accelerates parasitic reactions at the Ni-rich cathode, resulting in limited gains in cycling stability. By contrast, EC-free electrolytes with targeted additives, particularly 10% fluoroethylene carbonate (FEC), form more uniform SEI layers, suppress gas evolution, and significantly enhance cycling stability, achieving 95.5% capacity retention after 300 cycles. This work demonstrates the dual role of EC in Ni-rich systems and highlights how correlative operando methods can guide rational electrolyte design for long-lived, high-energy LIBs.

Genetic resources are becoming increasingly important in aquatic species, especially in sectors such as aquaculture and biomedical research. These advancements, however, lack standardized methodology to consistently improve efficient use of gametes for fertilization and to eliminate male variation during spawning. This study provides a conceptual basis for generalizable quality control in artificial spawning of aquatic species by using interdisciplinary, industrial-scale tools to calculate a fertilization unit (e.g., the amount of sperm required to reliably fertilize the eggs produced by a female). Blue catfish (Ictalurus furcatus), zebrafish (Danio rerio), and eastern oysters (Crassostrea virginica) were used as diverse representative species. Comparisons among aquatic species were reviewed, fertilization units were defined, and a sensitivity analysis was performed to assess how deviations from the fertilization unit could affect artificial spawning efficiency. Overall, reproductive strategy (e.g., gamete biology) and production setting significantly influenced the fertilization unit. Employing a fertilization unit decreased "wasted" sperm and reduced male variability during spawning. Furthermore, fertilization efficiency dropped significantly when sperm use strayed from the fertilization unit, declining with both underuse and overuse, especially in oysters and catfish. Standardizing gamete use in aquatic species is essential for economic planning and achieving commercial-scale production, especially when investing in selectively bred or cryopreserved sperm.

Hydrogen-electric propulsion systems (HEPS) are being developed to achieve net-zero flight in the near future. Key to accelerating the entry-into-service is the retrofit of HEPS onto existing airframes to reduce the certification timeline associated with clean sheet aircraft. Maintaining existing payload volume for operators of aircraft such as the Cessna C208B Grand Caravan will allow for a commercially viable solution. The volumetric energy density of compressed gaseous hydrogen or cryogenic liquid hydrogen and the increased cooling requirements of proton-exchange membrane fuel cells both lead to modifications mounted externally to the fuselage of the retrofitted aircraft. A 1:10 scale wind tunnel model was developed under a collaboration between the University of Bath and ZeroAvia. The additively manufactured model was assessed in a low-speed closed-return wind tunnel, with comparison to aerodynamic coefficients derived from publicly-available C208 data. The test article includes provisions for scaled propellers of 10" diameter with a range of pitch values. Simulated HEPS modifications of approximate size were tested on the belly of the aircraft, where an existing airframe cargo pod is mounted. The increased drag and modified trim of the C208 have implications on the design of the aerodynamic fairings that must contain the HEPS modifications.

The widespread dissemination of misinformation on online platforms has become a significant societal challenge, influencing public opinion, political landscapes, and social stability. Traditional rule-based and statistical methods for fake news classification often struggle to generalize across different datasets due to the evolving nature of misinformation. To address this, deep learning and natural language processing (NLP) techniques have emerged as effective solutions for detecting deceptive content. In this study, a novel fake news classification framework is proposed, integrating Transformer-based feature extraction with an XGBoost classifier. The methodology leverages RoBERTa embeddings, Term Frequency-Inverse Document Frequency (TF-IDF)-based tokenization, and metadata processing to capture both linguistic and contextual cues essential for accurate classification. The model is trained and evaluated on the PolitiFact and GossipCop datasets, achieving state-of-the-art performance with an accuracy of 0.9930 and 0.9764, respectively. Comparative analysis with existing methods demonstrates the effectiveness of our approach in improving precision, recall, and F1-score. The findings underscore the importance of combining deep learning-based feature extraction with ensemble learning techniques for robust and scalable fake news detection.

Unlike traditional centralized learning-which requires aggregating all raw data in a single repository-federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy by exchanging only model parameters. This paper presents a privacy-preserving FL framework deployed on a wireless ad hoc edge network of lowcost Raspberry Pi devices. A minimal Bash script automates the configuration of the MANET topology, ensuring scalability and resilience across embedded nodes. We implemented a lightweight TCP message protocol to transmit model parameters and control signals, keeping local datasets private throughout. To validate the framework, we employed the IBM Telco Customer Churn dataset-chosen for its complexity and imbalance-though the approach generalizes to other tabular problems. We systematically evaluated four aggregation methods (FedAvg, FedProx, FedNova, and FedAvgM) under both IID and non-IID partitions, and compared them to a centralized baseline trained on the entire combined dataset collected from all clients in one place. Results show that while FedNova excelled under balanced conditions, FedAvgM was more robust to heterogeneous client distributions. These findings highlight the practicality of deploying federated neural networks on infrastructure-free edge environments, achieving competitive performance without sharing sensitive raw data.

Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities to enhance patient care and healthcare systems. With AI technologies, including machine learning (ML) and natural language processing (NLP), healthcare organizations can optimize workflows, improve decision making, and deliver more personalized care. However, to fully realize AI’s potential, it is essential to integrate human factors (HF) principles and adhere to stringent regulatory frameworks that ensure both safety and trust. This paper describes examples of healthcare AI from the authors’ experience and from literature review, including electronic health record (EHR) applications and neuromorphics technologies, and then addresses relevant issues relating to automation trust theory and practice. Trust issues covered include users’ positive/negative attitudes stemming from knowing AI is the source of the information, as well as cybersecurity concerns related to the privacy and security of data generated or processed by AI, and ethical considerations affecting user trust. We summarize key solutions that support compliance with regulatory standards, and suggest future work.