Search Results (111)

The increasing demand for sustainably produced food has intensified interest in fermented milk products, such as yoghurt, which combine nutritional value with environmental and ethical considerations. However, the authenticity of sustainability claims in this sector remains contested, raising concerns about consumer trust and regulatory clarity. This review examines the role of certification and labelling in verifying and communicating the sustainability of fermented milk products. The analysis covers regulatory frameworks, consumer perceptions, and the potential of digital tools to improve transparency. Findings highlight inconsistencies in defining key terms such as organic, probiotic, and carbon-neutral, which hinder certification harmonization. Complex labels and allergen declarations can reduce clarity and trust, while overlapping or vague eco-labels risk contributing to consumer confusion and skepticism. Despite this, credible certifications still enhance purchase intent. Modern technologies, including blockchain traceability, interactive QR codes, and digital product passports, offer new ways to reinforce trust, though implementation costs and regulatory gaps remain barriers. This review concludes that effective sustainability communication must integrate robust certification schemes with simplified, transparent messaging. Harmonized standards, improved label design, and consumer education are essential to support informed choices and foster trust in sustainable dairy. Full article

The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation. Full article

This study examines the adoption of real-time visibility (RTV) technologies in the Australian meat cold supply chain, a sector where sustainability challenges such as food spoilage, energy inefficiency, and waste are acute. RTV technologies offer promising solutions by enhancing traceability, operational efficiency, and decision-making across supply chain stages. However, adoption remains uneven due to a range of contextual, organisational, and perceptual factors. Through a nationally distributed quantitative survey targeting stakeholders across inventory, logistics, and retail operations, we identify key drivers and barriers influencing RTV adoption. We explore how demographic factors (e.g., age, role), perceived usefulness and ease of use, and supply chain characteristics interact to shape adoption outcomes. Importantly, the study investigates how horizontal collaboration and data-sharing practices moderate these relationships, especially within the transport and logistics stages where cold chain vulnerabilities are highest. Spearman and partial correlation analyses, alongside binary logistic regression, reveal that perceived ease of use and usefulness are significant predictors of adoption, while barriers such as cost and technical complexity impede it. However, strong collaboration and data-sharing networks can mitigate these barriers and enhance adoption likelihood. Our findings suggest that targeted digital infrastructure investment, workforce training, and policy support for cross-organisational collaboration are essential for advancing sustainability in meat cold chains. This research contributes to a growing body of knowledge that connects technological innovation with food system resilience and waste minimisation. Full article

In the cybersecurity attack and defense space, the “attacker” and the “defender” form a dynamic and symmetrical adversarial pair. Their strategy iterations and capability evolutions have long been in a symmetrical game of mutual restraint. We will introduce modern Intrusion Detection Systems (IDSs) from the defender’s side to counter the techniques designed by the attacker (APT attack). One major challenge faced by IDS is to identify complex attack paths from a vast provenance graph. By constructing an attack behavior tracking graph, the interactions between system entities can be recorded, but the malicious activities of attackers are often hidden among a large number of normal system operations. Although traditional methods can identify attack behaviors, they only focus on the surface association relationships between entities and ignore the deep causal relationships, which limits the accuracy and interpretability of detection. Existing graph anomaly detection methods usually assign the same weight to all interactions, while we propose a Causal Autoencoder for Graph Explanation (CAGE) based on reinforcement learning. This method extracts feature representations from the traceability graph through a graph attention network(GAT), uses Q-learning to dynamically evaluate the causal importance of edges, and highlights key causal paths through a weight layering strategy. In the DARPA TC project, the experimental results conducted on the selected three datasets indicate that the precision of this method in the anomaly detection task remains above 97% on average, demonstrating excellent accuracy. Moreover, the recall values all exceed 99.5%, which fully proves its extremely low rate of missed detections. Full article

Elemental contents are effective fingerprints for Pu-erh tea’s geographical traceability, which is crucial for consumer protection and sustainable development. Region and processing methods are key factors influencing the tea’s elemental fingerprint. This study analyzed 28 elements in Pu-erh tea samples from three Yunnan production regions subjected to different processing stages in the year of 2023. The results show that significant regional differences were observed for 25 of the 28 elements. As, Li, Cu, Zn, and Cd contents vary significantly during processing. The contents of 27 elements (excluding Pb) are significantly influenced by the interaction between region and processing stage. Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved good validation (Q² = 0.946) and identified 18 key factors, while the original and cross-validation correct classification rates were 100% and 98.6%, respectively. Crucially, the robustness of the model was confirmed with 100% accuracy through an independent validation set from tea samples in the harvest year of 2024. This study confirms that the elemental contents of Pu-erh tea are mainly influenced by region rather than processing stage, and elemental analysis can trace the geographical origin of Pu-erh effectively, even when mixed with a differently processed tea. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

As Generation Z gradually becomes the dominant consumer group, the catering industry, as a critical sector affecting people’s livelihood, warrants an in-depth investigation into its consumption decision mechanisms. This study, grounded in the online–merge–offline (OMO) context, empirically examines the impact mechanism of frontline employee service competence on the repurchase decisions of Generation Z consumers in the foodservice sector, while testing the mediating roles of customer satisfaction and brand trust, as well as the moderating effect of the OMO scenario. Data were collected via a survey of 326 Generation Z customers who consumed in integrated OMO dining environments. Partial least squares structural equation modeling (PLS-SEM) was employed for the data analysis. The findings reveal that frontline employee service competence significantly and positively influences consumer repurchase intention and customer satisfaction, but does not have a significant effect on brand trust. Customer satisfaction fully mediates the relationship between employee service competence and repurchase decisions, whereas brand trust, despite having the strongest direct effect on repurchase intention, is predominantly shaped by systemic factors such as food safety and supply chain transparency, rendering its mediating pathway non-significant. Furthermore, the OMO context does not exhibit a significant moderating effect between employee service competence and customer satisfaction, nor between employee service competence and brand trust, reflecting that the current digital integration has yet to effectively address Generation Z’s core needs for privacy protection and emotional resonance. This study elucidates the “contact–cognition–behavior” pathway by which service competence influences consumer decision-making through satisfaction, while clarifying the systemic formation mechanism of brand trust. Based on these results, it is recommended that enterprises prioritize emotional service training for frontline employees to enhance satisfaction, build brand trust through ingredient traceability systems, and optimize OMO scenario design to better align with Generation Z’s expectations for emotional interaction. Full article

Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a significant portion of processing and production units but face challenges in digital transformation despite their importance. Technologies such as Artificial Intelligence (AI), blockchain, cloud services, IoT, and mobile platforms offer tools to improve efficiency, access, value creation, and traceability. However, the patterns and applications of these transformations in Agri-SMEs remain fragmented and under-theorized. This paper presents a systematic review of interactions between digital transformation and innovation in Agri-SMEs based on findings from ninety-five peer-reviewed studies. Key themes identified include AI-based decision support, blockchain traceability, cloud platforms, IoT precision agriculture, and mobile technologies for financial integration. The review maps these themes against business model values and highlights barriers like capacity gaps and infrastructure deficiencies that hinder scalable adoption. It concludes with recommendations for future research, policy, and ecosystem coordination to promote the sustainable development of digitally robust Agri-SMEs. Full article

The blockchain technology is transforming the mining industry by enabling mineral reserve tokenization, improving security, transparency, and traceability. However, controlling access to sensitive mining data remains a challenge. Existing access control models, such as role-based access control, are too rigid because they assign permissions based on predefined roles rather than real-world conditions like mining licenses, regulatory approvals, or investment status. To address this, this paper explores an attribute-based access control model for blockchain-based mineral tokenization systems. ABAC allows access permissions to be granted dynamically based on multiple attributes rather than fixed roles, making it more adaptable to the mining industry. This paper presents a high-level system design that integrates ABAC with the blockchain using smart contracts to manage access policies and ensure compliance. The proposed model is designed for permissioned blockchain platforms, where access control decisions can be automated and securely recorded. A comparative analysis between ABAC and RBAC highlights how ABAC provides greater flexibility, security, and privacy for mining operations. By introducing ABAC in blockchain-based mineral reserve tokenization, this paper contributes to a more efficient and secure way of managing data access in the mining industry, ensuring that only authorized stakeholders can interact with tokenized mineral assets. Full article

Background/Objectives: The overuse of short-acting β₂-agonists (SABAs) has been associated with increased asthma morbidity and mortality, prompting changes in treatment guidelines. However, the role of frequent short-acting muscarinic antagonists (SAMAs) use remains poorly defined and unaddressed in current recommendations. This study offers the first real-world analysis of SAMA overuse in asthma, quantifying its association with exacerbation risk and healthcare utilization and comparing its predictive value to that of SABAs. Methods: A retrospective multicenter cohort study analyzed electronic health records (EHRs) from 132 adults with asthma in the Spanish National Health System (SNS). Associations between annual SAMA use and clinical outcomes were assessed using negative binomial regression and 5000-sample bootstrap simulations. Interaction and threshold models were applied to explore how SAMA use affected outcomes and identify clinically actionable cutoffs. Results: SAMA use was independently associated with a 19.2% increase in exacerbation frequency per canister and a nearly sixfold increase in the odds of experiencing ≥1 exacerbation (OR = 5.97; 95% CI: 2.43–14.66). An inflection point at 2.5 canisters/year marked the threshold beyond which annual exacerbations exceeded one. Increased SAMA use was also associated with a higher number of respiratory consultations and with more frequent prescriptions of systemic corticosteroids and antibiotics. The risk increased more sharply with SAMAs than with SABAs, and the lack of correlation between them suggests distinct clinical patterns underlying their use. Conclusions: SAMA use emerges as a digitally traceable and clinically meaningful indicator of asthma instability. While the associations observed are robust and consistent across multiple outcomes, they should be considered provisional due to the study’s retrospective design and limited sample size. Replication in larger and more diverse cohorts is needed to confirm external validity. These findings support the integration of SAMA tracking into asthma management tools—alongside SABAs—to enable the earlier identification of uncontrolled disease and guide therapeutic adjustment. Full article

Practical Byzantine Fault Tolerance (PBFT) has been widely used in consortium blockchain systems; however, it suffers from performance degradation and susceptibility to Byzantine faults in complex environments. To overcome these limitations, this paper proposes RE-BPFT, an enhanced consensus algorithm that integrates a nuanced node credibility model considering direct interactions, indirect reputations, and historical behavior. Additionally, we adopt an optimized ID3 decision-tree method for node classification, dynamically identifying high-performing, trustworthy, ordinary, and malicious nodes based on real-time data. To address issues related to centralization risk in leader selection, we introduce a weighted random primary node election mechanism. We implemented a prototype of the RE-BPFT algorithm in Python and conducted extensive evaluations across diverse network scales and transaction scenarios. Experimental results indicate that RE-BPFT markedly reduces consensus latency and communication costs while achieving higher throughput and better scalability than classical PBFT, RBFT, and PPoR algorithms. Thus, RE-BPFT demonstrates significant advantages for large-scale and high-demand consortium blockchain use cases, particularly in areas like digital traceability and forensic data management. The insights gained from this study offer valuable improvements for ensuring node reliability, consensus performance, and overall system resilience. Full article

As the Internet of Things (IoT) continues to evolve, the demand for cross-domain collaboration between devices and data sharing has grown significantly. Operations confined to a single trust domain can no longer satisfy this requirement, so cross-domain access to resources is becoming an inevitable trend in the evolution of the IIoT. Due to identity trust issues between different domains, authorized access is required before resources can be shared. However, most existing cross-domain authentication schemes face significant challenges in terms of dynamic membership management, privacy protection, and traceability. These schemes involve complex and inefficient interactions and fail to meet the dynamic and lightweight requirements of the IIoT. To address these issues, we propose a privacy-preserving and traceable cross-domain authentication scheme based on dynamic group signatures that enables efficient authentication. The scheme supports anonymous authentication via succinct proofs and incorporates a trapdoor mechanism to enable group managers to trace and revoke malicious identities. Additionally, our solution supports efficient joining and revoking of members and implements blacklist-based proof of non-membership. We formally prove the security of the proposed scheme. The experimental results demonstrate that the proposed scheme outperforms others in terms of computational cost and revocation overhead. Full article

An important step toward automation and digitization in Industry 4.0 is the automobile sector’s use of smart manufacturing integrated systems (SMISs). Although this change increases productivity and competitiveness, it also creates new hazards for workplace safety. Key issues include ergonomic and cognitive strain from greater human–machine interactions, particularly with collaborative robots (cobots), and cybersecurity threats from the IIoT and cyber–physical systems. This paper looks at these hazards and stresses the value of safety precautions like predictive maintenance, traceability, and real-time monitoring. This case study investigates how the integration of smart manufacturing integrated systems (SMISs) and cyber–physical systems (CPSs) within Industry 4.0 frameworks enhances workplace safety in the automotive sector. Through a comprehensive case study of the final assembly line, this research explores how these technologies contribute to predictive maintenance, real-time monitoring, and human–machine collaboration, leading to significant reductions in ergonomic and cybersecurity risks. Full article

This study employs an evolutionary game theory framework to analyze the interactive learning, imitation, and strategic evolution among multiple actors within China’s aquatic product traceability system. It focuses on four types of strategic interactions: between fishers and the government, fishers and consumers, fishers who adopt the traceability system and those who do not, and between consumers who purchase traceable aquatic products and those who do not. The evolutionarily stable strategies and equilibrium outcomes in each game depend on the net benefits obtained and the various costs borne by each party. Among these factors, transaction costs within the traceability system play a particularly critical role in shaping stakeholder behavior. The lower the transaction costs, the more likely stakeholders are to adopt strategies that support or enhance the functioning of the system. Therefore, reducing the operational and transaction costs of the traceability system should be a key policy focus for the government. This includes efforts in policy and regulatory development, platform and infrastructure construction, and the improvement of information exchange mechanisms to foster sustainable development in aquaculture. Full article

Enhancing the safety standards of autonomous ships is a shared objective of all stakeholders involved in the maritime industry. Since the existing hazard analysis work for autonomous ships often exhibits a degree of subjectivity, in the absence of data support, the verification of hazard analysis results has become increasingly challenging. In this study, a formal verification method in a risk-based assessment framework is proposed to verify the hazard analysis results for autonomous ships. To satisfy the characteristics of high time sensitivity, time automata are adopted as a formal language while model checking based on the formal verification tool UPPAAL is used to complete the automatic verification of the liveness of system modeling and correctness of hazard analysis results derived from extended System-Theoretic Process Analysis (STPA) by traversing the finite state space of the system. The effectiveness of the proposed method is demonstrated through a case study involving a remotely controlled ship. The results indicate that the timed automata network model for remotely controlled ships, based on the control structure, has no deadlocks and operates correctly, which demonstrates its practicability and effectiveness. By leveraging the verification of risk analysis results based on model checking, the framework enhances the precision and traceability of these inputs into RBAT. The results disclose the significance of the collaborative work between safety and system engineering in the development of autonomous systems under the definition of human–computer interaction mode transformation. These findings also hold reference value for other intelligent systems with potential hazards. Full article