Search Results (28)

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

This paper proposes a hybrid collaborative framework to optimize technology selection in Small and Medium-sized Enterprises (SMEs) by integrating machine learning (ML) predictions with Planning Poker, consensus-based estimation technique used in agile software development. Addressing known challenges such as cognitive bias, resource constraints, and the need for inclusive decision-making, the proposed model combines data-driven suitability analysis with stakeholder-driven consensus. ML generates quantitative, criterion-wise suitability scores based on historical SME data, providing transparent baselines for evaluation. Stakeholders independently assess candidate technologies using Planning Poker, and their consensus is blended with ML predictions through a flexible weighting mechanism. An illustrative case study on CRM tool selection illustrates the framework’s practical advantages: improved decision accuracy, transparency, and greater stakeholder engagement. The methodology is iterative, allowing for continuous learning and adaptation as new data emerges. This dual approach ensures that technology adoption decisions in SMEs are both empirically validated and contextually robust, offering a significant improvement over traditional, siloed methods. Full article

With the extensive integration of renewable energy sources (RESs), power systems face challenges in load frequency control (LFC) due to RES intermittency. While energy storage systems (ESSs) aid frequency regulation, existing strategies are limited—single-type ESSs fail in multi-ESS scenarios, and hybrid ESSs lack state-of-charge (SoC) consistency control. This paper proposes an LFC framework combining energy storage aggregators (ESAs), leader–follower finite-time consensus control, and DDPG-RNN (Deep Deterministic Policy Gradient with Recurrent Neural Networks). ESAs aggregate small distributed ESSs for scalable regulation; consensus control ensures finite-time ESS power tracking and SoC balancing; and DDPG-RNN adaptively tunes control gains to handle RES fluctuations and load changes. Simulations on a high-RES power system with hybrid ESSs (SCES, LABES, VRFBES, LIPBES) show that the framework outperforms traditional proportional–integral–derivative (PID) control and basic leader–follower control: it reduces frequency deviation peaks, shortens recovery time, achieves SoC synchronization, and alleviates conventional generator power fluctuations. Full article

Background: Parent education is a key component of family-centred care in neonatal intensive care units (NICUs). It supports positive parent-infant interactions, reduces parental stress and anxiety, and contributes to shorter hospital stays. Objectives: This paper reports on the adaptation of a South African parenting education intervention for parents of premature infants in the NICU: the NeuroSense PremmieEd programme. The study aimed to demonstrate the programme’s content validity, feasibility, and acceptability for preterm parent–infant dyads in public hospital NICUs, using a hybrid focus group discussion (FGD) method. The programme was based on an existing intervention and informed by literature on the components of parenting educational programmes and empirical data on parental expectations. Methods: A qualitative, iterative refinement process was undertaken using hybrid-format FGDs. A conceptual FGD was held during the design phase, followed by two consensus FGDs after pilot testing (reported separately). Stakeholders included end-users (mothers), clinicians, an instructional designer, a neurodevelopmental care expert, and programme facilitators. Results: The first FGD reviewed draft version 0.1 of the programme, confirming content relevance and clarity, while recommending adjustments, such as module integration, cultural and language alignment, and visual aids to support comprehension. Version 0.2 was then ready for pilot testing (reported elsewhere). The second and third FGDs led to refinements addressing emotional sensitivity in terminology, improved layout and readability, and the addition of home care guidance. Stakeholders highlighted the potential use of low-cost digital formats to enhance accessibility and standardisation. These revisions informed the final version 0.3. Conclusions: The hybrid FGD approach enabled input from diverse and geographically dispersed stakeholders. The NeuroSense PremmieEd programme was found to be feasible and acceptable by both mothers and healthcare professionals, supporting its suitability for broader implementation in resource-constrained settings. Full article

To meet the growing demands for competency-based and personalized instruction in high school English reading, this study investigates a quantitative approach to modeling learning pathways and progressions. Traditional assessments often fail to capture students’ fine-grained cognitive differences and provide limited guidance for individualized teaching. Based on cognitive diagnostic theory, this study analyzes large-scale empirical data to construct a progression framework reflecting both the sequencing of cognitive skill development and the hierarchical structure of reading abilities. A Q-matrix was calibrated through expert consensus. A hybrid cognitive diagnostic model was used to infer students’ knowledge states, followed by cluster analysis and item response theory to define progression levels, which were mapped to national curriculum standards. The findings reveal that students’ mastery of cognitive attributes follows a stepwise developmental pattern, with dominant learning trajectories. The constructed learning progression aligns well with curriculum-based academic quality levels, while uncovering potential misalignments in the positioning of some skill levels. Students with identical scores also showed significant variation in cognitive structures. The proposed model provides a data-informed foundation for adaptive instruction and offers new tools for personalized learning in English reading comprehension. Full article

Background: Chronic Brain Injury (CBI) is a lifelong condition requiring continuous adaptation by patients, families, and healthcare professionals. Transitioning rehabilitation toward patient-centered and self-management approaches is essential, yet remains limited in Spain. Methods: We conducted a two-phase consensus study in collaboration with the Spanish Society of Physical Medicine and Rehabilitation (SERMEF) and the Spanish Federation of Brain Injury (FEDACE). In Phase 1, surveys were distributed to patients (214 invited; 95 complete responses, 44.4%) and physiatrists (256 invited; 106 valid responses, 41.4%) to capture perceptions of current rehabilitation practices, including tele-rehabilitation. Differences and convergences between groups were analyzed using a Synthetic Factor (F). In Phase 2, a panel of 21 experts applied a real-time eDelphi process (SmartDelphi) to assess the feasibility of proposed innovations, rated on a six-point Likert scale. Results: Patients and professionals showed both alignment and divergence in their views. Patients reported lower involvement of rehabilitation teams and expressed more reluctance toward replacing in-person care with telemedicine. However, both groups endorsed hybrid models and emphasized the importance of improved communication tools. Expert consensus prioritized feasible interventions such as online orthopedic renewal services, hybrid care models, and educational video resources, while less feasible options included informal communication platforms (e.g., WhatsApp) and bidirectional teleconsultations. Recommendations were consolidated into five domains: (R1) systemic involvement of rehabilitation teams in chronic care, (R2) patient and caregiver education, (R3) self-management support, (R4) communication tools, and (R5) socialization strategies. Conclusions: This study demonstrates the value of combining patient and professional perspectives through digital Delphi methods to co-design innovation strategies in CBI rehabilitation. Findings highlight the need to strengthen communication, provide structured education, and implement hybrid care models to advance patient-centered rehabilitation. The methodology itself fostered engagement and consensus, underscoring its potential as a tool for participatory healthcare planning. Full article

Teacher preparation programmes must now ensure instructional continuity and quality across face-to-face, online, and hybrid modes, even amid health, climate, or security crises. This mixed-methods study examined which principles policymakers and teacher education directors deem essential for such resilience, and how those principles align with prior research and leadership theory. Semi-structured elite interviews (N = 25) were analyzed inductively to surface field-driven themes and deductively through two models: the ten evidence-based training principles synthesized by Hadad et al. and the six capacities of Striepe and Cunningham’s Crises Leadership Framework (CLF). Results show strong consensus on theory–practice integration, university–school partnerships, and collaborative learning, mapping chiefly to the CLF capacities of adaptive roles and stakeholder collaboration. Directors added practice-oriented priorities—authentic field immersion, formative feedback, and inclusive pedagogy—extending the crisis care and contextual influence dimensions. By contrast, policymakers uniquely stressed policy–academic co-decision-making, reinforcing complex decision-making at the system level. Reflective thinking skills and digital pedagogy, though prominent in the literature, were under-represented, signalling implementation gaps. Overall, the integrated model offers a crisis-ready blueprint for curriculum design, partnership governance, and digital capacity-building that can sustain continuity and quality in pre-service teacher education. Full article

Bus voltage regulation and accurate power sharing constitute two pivotal control objectives in DC microgrids. The conventional droop control method inherently suffers from steady-state voltage deviation. Centralized control introduces vulnerability to single-point failures, with significantly degraded stability under abnormal operating conditions. Distributed control strategies mitigate this vulnerability but require careful balancing between control effectiveness and communication costs. Therefore, this paper proposes a hybrid hierarchical control architecture integrating multiple control strategies to achieve near-zero steady-state deviation voltage regulation and precise power sharing in DC microgrids. Capitalizing on the complementary advantages of different control methods, an operation-condition-adaptive hierarchical control (OCAHC) strategy is proposed. The proposed method improves reliability over centralized control under communication failures, and achieves better performance than distributed control under normal conditions. With a fault-detection logic module, the OCAHC framework enables automatic switching to maintain high control performance across different operating scenarios. For the inherent trade-off between consensus algorithm performance and communication costs, a communication topology optimization model is established with communication cost as the objective, subject to constraints including communication intensity, algorithm convergence under both normal and N-1 conditions, and control performance requirements. An accelerated optimization approach employing node-degree computation and equivalent topology reduction is proposed to enhance computational efficiency. Finally, case studies on a DC microgrid with five DGs verify the effectiveness of the proposed model and methods. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

Automated document processing and circulation systems face critical challenges in achieving reliable retrieval accuracy and robust classification performance, particularly in security-critical organizational environments. Traditional approaches suffer from fundamental limitations, including fixed fusion strategies in hybrid retrieval systems, inability to model inter-document relationships in classification tasks, and lack of confidence estimation for result reliability. This paper introduces AttenFlow, a novel context-aware architecture that revolutionizes document management through two core technical innovations. First, we propose the retriever consensus confidence fusion (RCCF) method, which addresses the limitations of conventional hybrid retrieval approaches by introducing consensus-based fusion strategies that dynamically adapt to retriever agreement levels while providing confidence estimates for results. RCCF measures the consensus between different retrievers through sophisticated ranking and scoring consistency metrics, enabling adaptive weight assignment that amplifies high-consensus results while adopting conservative approaches for uncertain cases. Second, we develop adversarial mutual-attention hybrid-dimensional graph attention network (AM-HDGAT) for text, which transforms document classification by modeling inter-document relationships through graph structures while integrating high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms. The approach incorporates adversarial training to enhance robustness against potential security threats, making it particularly suitable for critical document processing applications. Comprehensive experimental evaluation across multiple benchmark datasets demonstrates the substantial effectiveness of our innovations. RCCF achieves improvements of up to 16.9% in retrieval performance metrics compared to traditional fusion methods while providing reliable confidence estimates. AM-HDGAT for text demonstrates superior classification performance with an average F1-score improvement of 2.23% compared to state-of-the-art methods, maintaining 82.4% performance retention under adversarial attack scenarios. Real-world deployment validation shows a 34.5% reduction in manual processing time and 95.7% user satisfaction scores, establishing AttenFlow as a significant advancement in intelligent document management technology. Full article

The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments. Full article

In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind power prediction methods. Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. The analysis reveals that the data-driven prediction model dominates ultra-short-term predictions through rapid response to volatility, while the hybrid method enhances short-term precision. Mid-term predictions increasingly integrate climate dynamics to address seasonal variability. A key contribution lies in unifying fragmented methodologies into a decision support framework that prioritizes the time scale, model adaptability, and spatial constraints. This work enables practitioners to systematically select optimal strategies and advance the development of forecasting systems that are critical for highly renewable energy systems. Full article

With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design. Full article

The fairness problem in the IOTA (Internet of Things Application) Tangle network has significant implications for transaction efficiency, scalability, and security, particularly concerning orphan transactions and lazy tips. Traditional tip selection algorithms (TSAs) struggle to ensure fair tip selection, leading to inefficient transaction confirmations and network congestion. This research proposes a novel partially observable Markov decision process (POMDP)-based TSA, which dynamically prioritizes tips with lower confirmation likelihood, reducing orphan transactions and enhancing network throughput. By leveraging probabilistic decision making and the Monte Carlo tree search, the proposed TSA efficiently selects tips based on long-term impact rather than immediate transaction weight. The algorithm is rigorously evaluated against seven existing TSAs, including Random Walk, Unweighted TSA, Weighted TSA, Hybrid TSA-1, Hybrid TSA-2, E-IOTA, and G-IOTA, under various network conditions. The experimental results demonstrate that the POMDP-based TSA achieves a confirmation rate of 89–94%, reduces the orphan tip rate to 1–5%, and completely eliminates lazy tips (0%). Additionally, the proposed method ensures stable scalability and high security resilience, making it a robust and efficient solution for decentralized ledger networks. These findings highlight the potential of reinforcement learning-driven TSAs to enhance fairness, efficiency, and robustness in DAG-based blockchain systems. This work paves the way for future research into adaptive and scalable consensus mechanisms for the IOTA Tangle. Full article

Conventional droop control (a decentralized method to regulate power sharing by adjusting voltage–current slopes) in DC microgrids faces challenges in balancing precise current distribution, bus voltage regulation, and communication pressure, especially in distributed energy management scenarios. To address these limitations, this paper proposes an adaptive control strategy combining three layers: (1) Primary control achieves power sharing and voltage stabilization via U-I droop characteristics for distributed energy resources (DERs); (2) Secondary control corrects voltage deviations and droop coefficient imbalances through multi-agent consensus algorithms, ensuring global equilibrium; (3) Event-triggered consensus control minimizes communication pressure via a novel protocol with time-varying coupling weights and a hybrid trigger function combining state variables and time-decaying terms rigorously proven to exclude Zeno behavior (i.e., infinite triggering in finite time) using Lyapunov stability theory. Full article