Search Results (361)

Trustworthy digital credential management is increasingly important in LMS-connected higher-education information systems, yet institutions still lack controlled implementation-oriented evidence on how cloud-native service decomposition and blockchain-backed trust services influence deployment performance. This study develops and evaluates a cloud-native architecture that combines containerized microservices with Hyperledger Fabric-based permissioned ledger services and a Polygon-linked public-chain anchoring path for credential issuance, learning-record verification, and record validation. Unlike largely conceptual prior work, it benchmarks four functionally aligned deployment paths in a unified Kubernetes-managed testbed: a monolithic baseline, a microservices-only baseline, a Hyperledger Fabric-integrated variant, and a Polygon-linked anchoring path. The credential-service paths were evaluated under stepped workloads from 1000 to 20,000 scheduled virtual users. Evaluation focused on service-path latency, throughput, tamper-detection accuracy, and resource utilization. The microservices-only architecture achieved the lowest baseline latency (182 ms), Hyperledger Fabric maintained stable response times for trusted institutional workflows (352 ms at baseline and 485 ms at 20,000 virtual users), and the Polygon-linked anchoring path reached the highest observed service-path throughput (228 TPS) in the tested prototype. Both blockchain-integrated variants detected tampered credentials in all successfully processed tamper cases. Overall, the results show that cloud-native decomposition and ledger-backed trust and anchoring can support scalable and trustworthy credential services when platform choice aligns with institutional governance scope, verification audience, and deployment constraints. Full article

Smart grid private communication networks need to support heterogeneous services with varying requirements for reliability, security, bandwidth, and controllability. In such networks, service function chains (SFCs) can provide customized network services by deploying virtual network functions (VNFs) over a shared substrate infrastructure. However, sharing physical servers among different service categories may conflict with the physical isolation requirement between critical grid services and common grid services. To address this problem, this paper investigates physical-isolation-aware SFC deployment for smart grid private communication networks. We first formulate an integer nonlinear programming (INLP) model that maximizes the network resource usage revenue while considering server resource constraints, link bandwidth constraints, flow conservation constraints, virtual link mapping constraints, server energy consumption, and physical isolation constraints. The nonlinear constraints are then linearized into an integer linear programming (ILP) model, which can be solved by an optimizer and used as a benchmark. To reduce the computational cost, we propose a private-network-oriented service function chain isolation deployment (PNO-SSID) algorithm. The proposed algorithm selects a revenue-aware subset of SFC requests, determines the service category to be preferentially processed, selects server nodes based on VNF-layer traffic cost, deploys VNFs using a matching-game-based method, and maps virtual links based on shortest paths. Simulation results show that PNO-SSID requires much less execution time than CPLEX while achieving close revenue in small-scale cases. Compared with online profit maximization (OLPM) variants using different request preprocessing strategies, PNO-SSID achieves higher network resource usage revenue and request acceptance ratio under physical isolation constraints. A prototype platform based on a fifth-generation non-standalone private network and the OAI platform further validates the feasibility of server-level isolated core network service chain deployment under the considered service-category separation requirement. Full article

Water-soluble viscosity-modifying admixtures (VMAs) were initially introduced into cementitious materials to enhance cohesion, stability and resistance to bleeding and segregation. With the development of self-compacting concrete, underwater concrete, grouting materials and 3D-printed cementitious materials, VMAs have become increasingly important for regulating rheological behavior, workability retention, shape retention and construction processability. Recent studies further indicate that VMAs can affect not only fresh-state properties, but also hydration kinetics, early-age microstructure evolution, mechanical performance, transport behavior and long-term durability. This review systematically summarizes the types, action mechanisms, and performance effects of water-soluble VMAs in cementitious materials. Particular emphasis is placed on the relationships among the molecular structure, liquid phase viscosity enhancement, particle adsorption and bridging, polymer-chain entanglement, ion-responsiveness, admixture compatibility, and microstructure evolution. The review shows that the effects of VMAs are not governed solely by admixture type or dosage, but depend strongly on molecular mass, functional groups, substituent composition, charge characteristics, binder chemistry, and the pore solution environment. Finally, current research gaps and future directions are discussed, including quantitative structure–mechanism–performance relationships, applicability in low-carbon binders, service-life prediction, and application-oriented VMA design. Full article

Future 6G networks require higher performance and wider service coverage. Multi-domain Service Function Chain (SFC) deployment enables service provisioning across multiple network domains to meet these demands. However, when collaboration occurs among different network operators, privacy-preserving mechanisms are required to protect sensitive information such as internal topology and resource availability. Existing SIRM-based mechanisms, such as the Privacy-Preserving Deployment Mechanism (PPDM), address this challenge but suffer from structural limitations: PPDM performs whole-chain feasibility evaluation with extensive virtual occupation. This paper proposes a $\mathcal{B}$-Batch Sequential Deployment mechanism for privacy-preserving multi-domain SFC deployment. Instead of evaluating whole-chain feasibility at once, the proposed $\mathcal{B}$-Batch mechanism partitions each incoming SFC into fixed-size VNF batches and constructs a batch-level SIRM. This design confines virtual occupation to the current batch and reduces both its magnitude and duration while remaining fully compatible with the SIRM privacy model and the hierarchical multi-domain control architecture. A Deep Q-Network (DQN) is employed to learn substrate node selection policies based solely on SIRM-based state information, without exposing domain-internal topology or resource details. Simulation results on a three-domain AARNET substrate topology demonstrate that the proposed mechanism consistently improves deployment robustness under varying traffic intensities and SFC lengths, including short (3–6 VNFs), medium (6–9 VNFs), and long (9–12 VNFs) service chains. Compared with PPDM, the proposed $\mathcal{B}$-Batch mechanism achieves higher acceptance ratios under moderate-to-heavy traffic while reducing end-to-end delay and improving average substrate resource utilization. Node selection analysis further shows that smaller batch sizes preserve feasibility through compact node reuse, whereas larger batch sizes encourage broader substrate exploration. Overall, the proposed $\mathcal{B}$-Batch mechanism enhances feasibility preservation and deployment robustness in privacy-preserving multi-domain SFC orchestration. Full article

The massive heterogeneous data streams and stringent low-latency requirements in the Industrial Internet of Things (IIoT) pose new challenges for edge network resource management. This paper addresses the joint optimization problem of Service Function Chain (SFC) orchestration and resource allocation in edge gateway-assisted IIoT networks, formulated as a mixed-integer nonlinear programming (MINLP) model to minimize end-to-end latency and energy consumption while satisfying quality-of-service (QoS) constraints. To tackle this NP-hard problem and the challenges of partial observability in distributed environments, we propose the SFC Orchestration and Resource Allocation-based Multi-Agent Proximal Policy Optimization (SORA-MAPPO) algorithm. The algorithm adopts a centralized training with decentralized execution (CTDE) paradigm with an intelligent agent cooperation mechanism. Simulation results validate the effectiveness of the proposed scheme in complex IIoT scenarios. Full article

Hydrogen energy, as an important green energy source, is a crucial guarantee for achieving carbon neutrality and peak carbon emission. The anion exchange membrane (AEM) electrolysis cell combines the advantages of alkaline electrolysis cell and proton exchange membrane electrolysis cell and can employ non-precious metal catalysts combined with renewable energy, which is expected to break through the bottleneck of high production cost of green hydrogen. AEM water electrolysis combines the advantages of alkaline and proton exchange membrane water electrolysis for hydrogen production. It has the characteristics of high electrolysis efficiency, fast response rates, and low cost, and its considered one of the most promising renewable green energy hydrogen production technologies at present. AEM is a key component that provides OH^– ion conduction and blocks gas crossover, which directly affects the performance and service life of the AEM electrolysis water system. However, current AEMs face issues of low ion conductivity and poor stability. This review introduces the role of AEM in electrolytic cells, the performance requirements and evaluation parameters that high-performance AEM should meet, and focuses on the transport mechanism and influencing factors of OH^– in AEM. Furthermore, this review provides an overview of the structural composition of AEM, as well as common cationic groups and polymer backbone types. The degradation mechanism of various cationic groups and the characteristics of polymer main chains were elaborated, with a focus on the strategies for designing the stability of cationic functional groups, the methods for modifying and preparing polymer main chains, and the performance of AEMs. Finally, the future challenges and potential research directions of AEM membranes are discussed. It is suggested that high-performance AEMs meeting practical application needs should be developed through strategies such as crosslinking, block copolymerization, side chain grafting, and composite membrane technology, based on the design of alkali-resistant and stable AEM membranes. These insights provide reference and guidance for the further development of AEMs. Full article

Late deliveries represent one of the most persistent operational disruptions in global supply chains, eroding service reliability, triggering contractual penalties, and undermining the resilience of logistics networks. As supply chains become increasingly digitalized, the integration of advanced predictive analytics into operational decision-making offers a pathway toward proactive rather than reactive disruption management. This study develops and evaluates a digital analytics framework in which eXtreme Gradient Boosting (XGB), a high-performance ensemble learning algorithm, is optimized by three recent population-based metaheuristic algorithms: the weighted mean of vectors algorithm (INFO), Harris Hawks Optimization (HHO), and the Red-Billed Blue Magpie Optimizer (RBMO). Four critical XGB hyperparameters, number of estimators, maximum tree depth, learning rate, and complexity penalty, are tuned on a supply chain dataset. A population-size sensitivity analysis at two swarm configurations reveals that all three optimizers converge to functionally equivalent solutions at sufficient population diversity, providing practical guidance for computational resource allocation. The best-performing configuration, HHO-XGB, achieves a test accuracy of 97.47% and a Matthews correlation coefficient of 0.949, substantially outperforming the baseline XGB and other benchmark classifiers. To ensure transparency and support data-driven decision-making, SHapley Additive exPlanations (SHAP) analysis is applied to the optimized model, revealing that shipping mode, scheduled shipment days, shipping date, order day, order status, and order month are the dominant predictive features, confirming that late-delivery risk is primarily driven by shipment configuration and temporal patterns. The proposed framework demonstrates that integrating metaheuristic intelligence with machine learning delivers better predictive performance. Interpretability is essential to trustworthy, resilient supply chain decision-support systems. Full article

Background: Studies of fish supply chain efficiency often rely on price spreads or frontier-based measures, which do not fully capture actor-level coordination performance in heterogeneous, informal supply chains. This study addresses this gap by developing a composite Market Efficiency Index (MEI) that integrates financial performance, operational quality, service equity, and relational governance. Methods: The MEI, a multidimensional alternative to frontier-based measures, was developed and applied to data collected from 250 supply chain actors in Sri Lanka. Results: The results show a clear efficiency gradient along the supply chain, with fishers scoring the lowest (MEI = 0.44), intermediaries moderate (MEI = 0.54), and retailers the highest (MEI = 0.67), yielding an overall system efficiency of 0.55 and relational governance emerging as the weakest system-level dimension. These results indicate persistent structural differences in value distribution and in how well the fish supply chain functions as a cohesive network, driven by liquidity constraints, information asymmetry, and weak cold-chain infrastructure. Conclusions: A multidimensional supply chain assessment provides a more effective basis for diagnosing coordination constraints and enables targeted digital interventions that offer feasible pathways to improve transparency, liquidity, and inclusiveness in smallholder-dominated fish supply chains. Full article

With decarbonization, decentralization, and digitalization, energy coordination increasingly involves many actors, heterogeneous cyber–physical data, and compliance-sensitive settlement workflows. Although blockchain has been widely discussed in this domain, existing studies are still fragmented across application-specific or platform-specific narratives. As a result, it remains difficult to compare recurring mechanisms across scenarios or to determine which blockchain functions are operationally justified in deployable energy systems. We address that fragmentation through a structured narrative review of 41 representative sources, including prior surveys, foundational technical references, and scenario-specific studies. We formulate three research questions concerning architectural positioning, cross-scenario mechanisms, and deployment barriers. On this basis, we synthesize a unified five-layer reference architecture that links off-chain physical infrastructure and trusted data acquisition to protocol-level trust anchoring, reusable business services, interface and compliance functions, and application scenarios. The framework is then used to compare five recurring scenario families, namely peer-to-peer energy trading, carbon markets and renewable energy certificates, electric vehicle charging and vehicle-to-grid services, virtual power plants, and grid flexibility coordination. The analysis shows that blockchain is most defensibly positioned as an evidence-and-settlement trust layer, rather than as a replacement for real-time physical control. It also identifies three persistent adoption bottlenecks, namely scalable ledger interaction, trustworthy cyber–physical data binding, and interoperability with regulatory and operational infrastructures. By making the trust boundary explicit and by providing a common analytical lens for cross-scenario comparison, this review clarifies the scientific contribution of blockchain to energy systems and outlines stakeholder-oriented directions for deployable hybrid designs. Full article

Freshwater scarcity is emerging as a critical constraint on industrial clusters, production networks, and urban service systems, where water functions simultaneously as an essential production input and a shared regional resource. This study investigates how post-allocation water-quota imbalances in large inter-basin diversion systems can be addressed through adaptive secondary water rights trading. Focusing on China’s South-to-North Water Diversion Project (SNWDP), the research aims to explain under what institutional and efficiency conditions water rights trading can enhance corporate social responsibility, environmental management, and sustainable supply chain resilience. The study’s main innovation lies in the development of a corporate-oriented evolutionary game model that links water governance with corporate production, urban–industrial demand, and responsible supply chain management. Unlike conventional models, it incorporates bounded rationality, heterogeneous water-use efficiency, information asymmetry, transaction costs, primary allocation water pricing, and the risk of unrecovered basic water fees. Using a case inspired by the Zhengzhou–Nanyang transaction along the Middle Route of the SNWDP, the model simulates the strategic interaction between a water-rich node with surplus quota and a water-scarce node facing deficit demand. The findings show that a socially desirable Trade–Trade equilibrium emerges only when efficiency expectations and institutional conditions are favorable. Lower transaction costs and basic water prices, higher sunk-fee risk, and clearer efficiency differentials significantly increase trading willingness. The study demonstrates the practical value of transparent secondary water markets in improving allocative flexibility, reducing governance rigidity, and promoting more responsible and environmentally efficient regional water management. Full article

Supply chains are increasingly exposed to compounding disruptions, volatile demand, and sustainability constraints, which challenge optimization approaches designed for stable operating conditions. This review synthesizes recent advances in supply chain optimization with a focus on the integration of artificial intelligence and operations research in decision-making. The paper examines three major capability layers: prescriptive optimization for planning and resource allocation, predictive modeling for demand and risk anticipation, and digitalized execution through simulation and digital twin environments. Across these layers, the analysis shows that hybrid AI-OR architectures tend to outperform isolated methods in settings characterized by high demand volatility, multi-echelon complexity, and disruption exposure, by combining predictive adaptability with constraint-aware decision quality. The review also highlights a strategic shift from single-objective efficiency toward multi-objective performance that jointly manages cost, service, resilience, and environmental impact. From an implementation perspective, the evidence indicates that measurable industrial gains depend less on algorithm novelty alone and more on system-level integration, data governance, and cross-functional deployment. Key research gaps remain in benchmark standardization, explainability, uncertainty-aware optimization, and long-horizon validation under disruption. The paper concludes that the next generation of supply chain optimization will be defined by continuously learning, human-supervised decision ecosystems that remain robust under uncertainty while delivering operational and sustainability outcomes. Full article

Network function virtualization (NFV) enables flexible service deployment by implementing network functions as software, with service function chains (SFCs) linking virtual network functions (VNFs) in a specific order to deliver end-to-end services. However, ensuring SFC resilience against large-scale disasters that can disrupt entire disaster zones (DZs) remains a significant challenge. In this paper, we study the multipath disaster-resilient SFC deployment problem, aiming to minimize the total bandwidth and computing resource overhead by jointly optimizing VNF placement, multipath routing, and protection mechanisms, subject to DZ-disjoint constraints. We formulate this problem as a Mixed-Integer Nonlinear Programming (MINLP) model and prove it to be NP-hard. To solve it efficiently, we propose a two-stage adaptive deployment approach; the first stage employs heuristic rules to generate a set of candidate paths satisfying DZ-disjoint constraints, and the second stage leverages deep reinforcement learning to intelligently place VNFs along these candidate paths, approximating the global optimum. Simulation results on real network topologies demonstrate that, compared to traditional dedicated protection strategies and a state-of-the-art exact algorithm, the proposed approach reduces resource overhead by up to 20% while effectively guaranteeing SFC disaster resilience, exhibiting good scalability and online deployment potential. Full article

Digital twins (DTs) in mobile edge computing (MEC) networks create virtual entities for IoT devices to provide query services. The required data is routed by a service function chain (SFC)—an ordered sequence of virtual network functions (VNFs). Quality of service (QoS) depends on data freshness, measured by Age of Information (AoI), and service makespan. However, due to limited DT update budgets and edge computing resources, waiting for DT updates for fresher data leads to higher service makespan; balancing data freshness and service makespan to maximize QoS is challenging, but can be done by determining DT updates and VNF scheduling. In this paper, we consider the problem of AoI-aware VNF scheduling with DT updates. We first formulate it as an integer nonlinear programming and prove it is NP-hard. Then, we propose an improved genetic algorithm featuring three-layer chromosome encoding, hybrid initialization, adaptive crossover and mutation, and a repair mechanism. Extensive experiments demonstrate the superiority of the proposed algorithm over baseline methods. Full article

The integration of service function chains (SFCs) and unmanned aerial vehicles (UAVs) lays a crucial technological foundation for achieving efficient, reliable, and adaptive future airborne service networks. Service functions (SFs) in the SFC will be deployed on UAVs; this type of SFC is called unmanned aerial vehicle-based service function chains (USFCs). However, due to the combined effects of open hardware and software architectures, exposed communication links, and complex mission environments, UAVs have become ideal targets for attackers. Once a vulnerability is successfully injected into a UAV, data from the SFs running on it will be stolen, seriously threatening the dependability of the USFC. Therefore, it is necessary to conduct a quantitative evaluation of the USFC dependability to provide insights for further improving its dependability. This paper develops a USFC dependability evaluation model based on a semi-Markov process (SMP) to capture the dynamic interaction between attacker behavior and USFC system recovery behavior. The dependability of the USFC is comprehensively evaluated from two perspectives: availability and security. Extensive numerical analysis experiments are conducted, and the results not only demonstrate the changing trends of various dependability metrics under different parameters but also show parameter combinations for synergistic optimization among metrics. Full article

Background: Last-mile logistics is one of the most complex and cost-intensive segments of supply chains, particularly in densely populated urban environments where rising customer expectations, sustainability requirements, and operational constraints increasingly intersect. Despite growing academic interest, empirical evidence remains limited regarding how convenience-related last-mile service attributes influence customer satisfaction, while the sector is undergoing a revolutionary transformation. Methods: This study applies a refined Kano model to classify last-mile convenience services according to their differentiated effects on customer satisfaction. Data were collected through a structured questionnaire administered to active e-commerce users in a metropolitan area. The methodological approach modifies and extends the traditional Kano framework. Results: The findings reveal clear patterns among last-mile service attributes. Online tracking and preferred payment options function as One-dimensional attributes, proportionally influencing customer satisfaction. Time-based delivery, flexible pickup options, and sustainability-oriented service features appear as Attractive attributes, generating additional increases in service value. In contrast, advanced technological solutions such as drone or autonomous vehicle delivery were perceived as Indifferent attributes. These interpretations are further nuanced by the fuzzy approach. Conclusions: The results provide important insights and validation for consumer-centered service design and support the prioritization of investments aimed at developing sustainable and customer-oriented last-mile logistics systems. Full article