Search Results (562)

Accurate short-term wind power forecasting is essential for the secure and economic operation of power systems with high renewable energy penetration. However, forecasting performance is still affected by meteorological forecast uncertainty and the complex multi-scale fluctuation characteristics of wind power generation. To address these challenges, this paper proposes an Initial-State-Aware Multi-Scale Transformer framework for 12 h-ahead wind power forecasting. The principal methodological contribution is an initial-state-aware meteorological representation and progressive fusion strategy tailored to weather-driven wind power forecasting. The framework explicitly distinguishes the atmospheric state available at forecast initialization from the subsequent forecast meteorological trajectory and uses the former to condition the representation of the latter through cross-attention and gated residual fusion. The resulting meteorological representation is then progressively coupled with coarse- and fine-scale historical power representations, and a horizon-oriented forecasting head generates the future power sequence in parallel. Experiments on three wind farms demonstrate that the proposed method achieves the best overall forecasting performance. Compared with the strongest baseline model, it reduces NMAE and NRMSE by 8.54% and 2.36%, respectively. Full article

Blockchain consensus still forces a hard choice among scalability, security, and energy use. Redbelly, a leaderless Byzantine Fault Tolerance protocol, largely settles the scalability-versus-energy side of that tension, yet it accepts any cryptographically valid transaction without judging whether it is economically fraudulent or whether the node behind it is misbehaving. That blind spot is what we target. We present ML-Redbelly, a formally specified extension that attaches four learning components to the Redbelly pipeline: a LightGBM gradient-boosted fraud classifier, an Isolation Forest behavioural anomaly detector, a tabular Q-Learning agent for adaptive committee selection, and a Paillier-based federated learning aggregator that keeps model updates private. We prove that this layer leaves Redbelly’s safety and liveness intact, give pseudocode and complexity bounds for every component, and measure the system on the IEEE-CIS Fraud Detection benchmark (400,000 transactions) paired with a faithful discrete-event Redbelly simulator parameterised from measured inputs and validated against the published Redbelly deployment. LightGBM reaches an F1 of 0.783 (precision 0.858, recall 0.719, AUROC 0.963), a 34 percent relative F1 gain over the conference-baseline Random Forest at five times the inference speed. The Isolation Forest detector attains recall 0.885 at a false-positive rate of 0.047, and the Q-Learning agent settles into a stable policy within about 200 rounds across normal, bursty, and Byzantine-attack conditions. End to end, the framework sustains 48,844 TPS on 32 validators (mean over 30 seeds), and because the leaderless superblock commits every proposer’s block in parallel, this throughput advantage over leader-based BFT grows with the validator count (5.0 times PBFT and 2.9 times HotStuff at 32 validators). The learning layer costs only about 4 percent in throughput, since the measured ML inference is small next to the geo-distributed consensus round. Per-transaction energy is comparable across BFT protocols, being dominated by signature verification, and is orders of magnitude below proof-of-work chains, which expend energy on mining. One federated update epoch takes 36 s across 10 nodes with 2048-bit Paillier keys and reconstructs gradients with negligible error. All performance figures are emergent outputs of the discrete-event simulation, which reproduces the published Redbelly benchmark to within a conservative factor of about 1.7. Taken together, these results outline a simulation-validated design for making consensus intelligent as well as fast and identify the steps needed toward real-cluster deployment. Full article

The Palace of Galiana in Toledo stands within the former royal suburban estate, or almunia, of al-Maʾmūn, ruler of the taifa of Toledo. Following the Christian conquest of the city in 1085, the estate passed to Alfonso VI and subsequently became known as the Huerta del Rey. The surviving residential hall has been attributed to the eleventh-century taifa period on typological and historical grounds, although this attribution still awaits archaeological and fabric-based analysis. This article examines the Mudéjar plasterwork and mural paintings added to the building under Christian rule in order to clarify their chronology, stylistic context, and visual function. The study combines formal and iconographic analysis, parallels with Andalusi and Mudéjar precedents, available technical evidence, and heraldic and documentary sources. The ground-floor plasterwork can be dated more securely to the late fourteenth century, probably around or shortly after 1397, on the basis of converging stylistic, heraldic, and documentary evidence. The painted dados on the upper floor present a more complex chronological problem: the available technical evidence does not provide an independent absolute date, while stylistic and stratigraphic evidence favors a broad chronology within the Christian period, probably between the thirteenth and fourteenth centuries and suggests that the surviving fragments may represent more than one decorative phase. The spatial distribution of the decoration also reveals a close relationship between ornament, views, water, and landscape. Galiana thus offers a significant example of the ways in which Andalusi and Christian artistic traditions were combined and transformed in the Christian-period redecoration of an earlier palatial setting of probable Andalusi origin. Full article

Blockchain-based infrastructures have increasingly been adopted for secure and tamper-resistant management of academic credentials. Despite the advantages offered by blockchain technology, existing blockchain-based credential management systems continue to face several scalability challenges, particularly in terms of limited transaction throughput, increased confirmation delays, and continuous ledger growth resulting from storing individual certificates as separate blockchain transactions. These limitations become more evident in large-scale educational environments where universities and affiliated institutions are required to issue and verify thousands of digital credentials within limited operational timeframes. To overcome these challenges, this work introduces a performance-optimized blockchain architecture for scalable academic credential management. The proposed framework separates certificate preprocessing from blockchain anchoring by incorporating a microservice-based parallel processing layer, Merkle-tree-based batch anchoring, and distributed off-chain storage mechanisms. This modular design reduces blockchain transaction overhead while maintaining the security, integrity, auditability, and verifiability of academic credentials. To assess system performance, a formal analytical model integrating queueing theory and blockchain performance characteristics is developed to characterize system behavior under varying workload conditions. By enabling multiple certificates to be aggregated and committed through a single blockchain transaction, the proposed architecture improves throughput and enhances storage efficiency compared to conventional blockchain-based approaches. Analytical evaluation demonstrates that the system can sustain high certificate issuance rates while maintaining low confirmation latency and minimal on-chain storage growth. Full article

Unlike conventional models that typically rely on a single memristive synapse, this study uniquely proposes a novel 5D fractional-order memristive Hopfield neural network (FOMHNN) modulated by dual memristors to simultaneously emulate internal synaptic plasticity and external electromagnetic radiation effects in brain-like computing. Analytically, the FOMHNN features multiple parallel lines of equilibria with double-zero eigenvalues, rigorously proving the generation of hidden attractors. The continuous dynamical behaviors are systematically evaluated using the Adomian Decomposition Method (ADM), revealing rich phenomena including transient chaos, grid multi-scroll hidden attractors, and frequency-controllable extreme multistability with fractal-like basin boundaries. The theoretical model is physically validated on a Field Programmable Gate Array (FPGA) platform, demonstrating high precision and ultra-low power consumption. To bridge theoretical dynamics with cryptographic applications, a novel pseudo-random number generator is designed. By incorporating a chaotic derivative extractor, the generated sequences significantly reduce topological periodicity, successfully passing all rigorous NIST SP 800-22 statistical tests. Furthermore, an adaptive color image encryption scheme is developed, utilizing bidirectional feedback diffusion and least significant bit (LSB) key embedding. Security analyses confirm that the cipher, under the fractional order $q=0.95$, achieves near-ideal information entropy, optimal resistance against differential attacks, with NPCR and UACI values reaching 99.6114% and 33.4910%, both extremely close to their theoretical ideals (99.6094% and 33.4635%), and robust resilience against noise. Ultimately, the FOMHNN provides a highly secure and physically realizable chaotic source for advanced secure communications. Full article

As offshore wind turbines continue to grow in capacity, reducing the structural weight of substructures has become a key factor in securing project economics, and the package installation method has attracted attention as a promising technology for reducing installation costs. This study presents an integrated design procedure for the package installation substructure of a 10 MW fixed offshore wind turbine, ranging from the establishment of classification rule-based design load conditions to surrogate model-based approximate optimization and verification of the optimum designs. Design load conditions for the installation, operation, and survival phases were defined in accordance with DNV classification rules and IEC 61400-3-1, and the structural safety of the initial design was evaluated via finite element analysis. Response data for 243 design matrices were generated using an orthogonal array design in which the thicknesses of nine primary structural members were defined as three-level design variables. Kriging, response surface methodology (RSM), and radial basis function neural network (RBFN) surrogate models were compared using multiple statistical metrics, and the RBFN, exhibiting the highest average coefficient of determination of 0.942 together with the lowest error levels for the stress responses, was selected for the approximate optimization. Discrete and continuous design variable optimizations were carried out in parallel by coupling the RBFN surrogate model with the adaptive simulated annealing (ASA) algorithm. The discrete optimum design reduced the structural weight by 2.5% while satisfying the allowable stress criteria for all load conditions, converged with approximately 94% fewer design evaluations than the continuous approach, and is defined directly in manufacturable plate thicknesses; it was therefore adopted as the final design. Full article

Overseas land investment has emerged as one of the most complex human–environment system challenges of the twenty-first century, yet its evolving role under global carbon-neutrality targets remains theoretically under-specified. This study conducts a systematic literature review and bibliometrically assisted analysis of 145 English and 38 Chinese publications (2008–2026) to map the intellectual landscape, identify divergent research priorities, and examine the implications of carbon-credit-based land investment. Our analysis reveals three distinct evolutionary phases, extending from conceptual emergence and mechanism analysis to interdisciplinary expansion. It also identifies three parallel divergences between Chinese- and English-language research: contrasting research stances, thematic priorities, and governance imaginaries. These divergences are rooted in China’s transitional position within a Western-dominated discourse system. The findings suggest that China’s transition from rule adaptation to active governance participation is important, and that priority pathways include property-rights protection, carbon-standard alignment, and polycentric governance frameworks capable of balancing food security, host-country development rights, and global ecological justice. These findings provide both theoretical synthesis and actionable directions for governing land-based carbon markets in an era of geopolitical fragmentation. Full article

The Post Office Horizon scandal represents one of the most severe miscarriages of justice in modern British legal history, rooted in the deployment of a defective IT system and the institutional suppression of evidence that exposed its unreliability. This paper provides a systematic analysis of the scandal through the combined lenses of cyber security, digital forensics, and IT governance, drawing directly on the Post Office Horizon Public Inquiry dataset—including witness testimony, technical documentation, audit records, and internal communications spanning more than two decades. While many of the technical and procedural failures discussed have been documented in prior scholarship, the paper’s principal contribution is the systematic mapping of those failures against recognised governance frameworks and the Legally Accountable Digital Systems (LADS) proposal this mapping motivates. We identify and analyse five interconnected failure categories: software defects and poor system design; deficient patch governance; inadequate audit logging and compromised evidence integrity; investigative failures and prosecutorial conflict of interest; and a systemic absence of technical expertise and independent oversight. Each category is mapped against nine governance framework documents, including ISO/IEC 27001, NIST SP 800-53 Rev. 5, NIST SP 800-218 (SSDF), COBIT 2019, ISO/IEC 27035, NIST SP 800-61, and ISO/IEC 27036, with ISO/IEC 27037 applied additionally to evidential handling failures and NIST SP 800-92 to log management failures. A counterfactual analysis indicates that compliance with these frameworks could plausibly have detected, exposed, or substantially reduced most of the documented failures, though this claim is necessarily inferential and conditional on good-faith implementation. The scandal was therefore not caused primarily by the absence of adequate frameworks but by their wholesale non-application and by institutional incentives, examined later in the paper, that can undermine even fully compliant controls. However, the analysis also surfaces a governance gap that no existing framework addresses: the institutional failure mode in which the organisation responsible for system integrity holds active incentives to suppress evidence of failure rather than remediate it. To address this gap, we propose the concept of Legally Accountable Digital Systems (LADS)—a governance category for systems whose outputs are used as evidence in legal proceedings—and outline three supplementary pillars: technical independence, institutional independence, and forensic admissibility governance. We draw a parallel with the Sarbanes–Oxley Act of 2002, arguing that the Horizon Inquiry dataset provides an equivalent empirical foundation for the statutory reform of digital evidence governance. Finally, we outline the substantial research opportunities the Inquiry dataset presents across IT systems analysis, cyber security, forensic accounting, social network analysis, and legal informatics—a resource comparable in significance to the Enron materials that shaped a generation of corporate governance reform. Full article

Tajikistan is highly vulnerable to climate change and depends heavily on agriculture, making soil moisture dynamics critical for water and food security. This study provides a comprehensive assessment of soil moisture variability across four depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) from 2000 to 2021 using NASA’s GLDAS-2 model and remote sensing data for land-air temperature, precipitation, and vegetation to identify key nexus of soil moisture change. Moisture data were converted to volumetric water content (m³/m³) to enable valid cross-layer comparisons. Our findings show that volumetric soil moisture increases with depth, from 0.219 m³/m³ at the surface to 0.293 m³/m³ in the deepest layer. Eastern Tajikistan exhibits higher moisture levels than the west, likely due to differing precipitation patterns. Seasonally, spring replenishes the soil with the highest moisture (0.270 m³/m³ at 0–10 cm), while summer strips it away (0.194 m³/m³ at 0–10 cm), potentially reflecting evapotranspiration losses. A significant warming trend is evident, with mean annual temperature peaking at 4.32 °C in 2016. Precipitation strongly influences upper-layer moisture (correlation: 0.49 at 0–10 cm; 0.44 at 10–40 cm). While annual averages remain stable, seasonal trends reveal significant winter wetting (+0.00043 m³/m³ per year, p < 0.001) and summer drying in the deepest layer, indicating intensifying seasonal contrasts. Vegetation follows a parallel pattern, declining from 2000 to 2010 and recovering thereafter. Greening is observed in 16.74% of the area, concentrated in the western mountains and northern highlands, while only 2.98% shows decline, mostly in small, fragmented patches. These findings highlight the substantial connection between climate, soil moisture, and vegetation in Tajikistan. They also suggest the need for depth-specific and seasonally aware water management strategies in this climate-sensitive region. Managing water here means looking beyond surface averages and thinking in layers, seasons, and geography. Full article

Arabic-speaking populations face disproportionate cybersecurity risks due to the predominantly English-centric design of existing awareness materials, which fail to accommodate Arabic dialectal diversity, script complexity, and culturally embedded communication patterns. These deficiencies impair users’ ability to interpret phishing messages, authentication requests, and security alerts, increasing susceptibility to social engineering, identity theft, and data breaches. This paper presents SECURE-A²RC, a rubric-constrained, Arabic-aware large language model framework designed to deliver scalable, interpretable, and culturally relevant cybersecurity education. The framework comprises two coupled components. The first, the Arabic-Aware Secure Communication Encoder (A-SCE), employs an instruction-tuned LLM to produce multidimensional encodings that capture three learner competencies: security intent comprehension; linguistic deception cue recognition encompassing urgency, authority impersonation, and incentive framing; and action-critical translation fidelity across Arabic dialectal registers and Arabic–English bilingual contexts. The second, the Rubric-Constrained Adaptive Feedback Generator (RCAFG), translates A-SCE encodings into personalized, expert-aligned instructional feedback and proficiency-calibrated adaptive tasks, ensuring pedagogical consistency, security correctness, and dialect awareness throughout the learning cycle. The framework is evaluated on three domain-relevant corpora: the English–Arabic Parallel Phishing Email Corpus, the Open MalSec dataset, and the Arabic Spam and Ham Tweets dataset. SECURE-A²RC achieves a 31% improvement in phishing identification accuracy and a 26% reduction in action-critical translation errors compared to conventional awareness materials. A comparative evaluation against SERENA, a Multi-Agent LLM, and the Arabic Multitask Learning Model confirms consistent superiority across detection accuracy, F1-score, dialectal robustness, and educational effectiveness metrics, affirming rubric-constrained LLM integration as a viable approach to equitable multilingual cybersecurity education. Full article

The dispatching data network and information management system in smart grids constitute a critical communication infrastructure that requires continuous security monitoring. However, network attacks exhibit multi-scale temporal characteristics ranging from microsecond-level bursts to slow intrusions lasting minutes, making single-scale detection models insufficient. Moreover, the hyperparameter space of deep learning models is large and highly non-convex, rendering traditional manual tuning inefficient. To address these challenges, this paper proposes an intrusion detection method based on the Dynamic Social Gray Wolf Optimizer (DSGWO) and the Multi-Scale Temporal Convolutional Network (MSTCN). The DSGWO maintains population diversity via an underdog alliance and breaks elite monopoly through a rank promotion challenge mechanism, balancing exploration and exploitation to avoid premature convergence. The MSTCN employs multi-scale parallel branches whose key training hyperparameters are optimized by the DSGWO, with residual connections and feature fusion for robust temporal modeling. Experiments on UNSW-NB15 and CIC-IDS-2017 demonstrate that DSGWO-MSTCN achieves F1-scores of 0.9933 and 0.9892, respectively, outperforming GWO, PSO, and NGO-based optimization approaches. Full article

As deep neural networks continue to scale and enable emerging applications such as agentic AI systems, training increasingly relies on distributed paradigms across heterogeneous edge devices. However, this shift introduces significant security challenges, particularly model poisoning attacks, which are largely underexplored in model-parallel settings. To address these challenges, we propose a trusted and attack-resilient mechanism for distributed DNN training that supports both data and model parallelism. The mechanism leverages a blockchain-enabled infrastructure to ensure the tamper-resistant and auditable execution of security-critical operations. It introduces a Loss-aware Credit Evaluation mechanism to assess agent reliability based on group-level training dynamics and a Shuffling-based Isolation Mechanism to progressively cluster and isolate malicious agents across training epochs. In addition, Byzantine-tolerant aggregation (BTA) is employed to further mitigate adversarial influence during model aggregation. Extensive experiments demonstrate that the proposed mechanism achieves superior robustness and efficiency compared with state-of-the-art methods under diverse poisoning attack scenarios. Full article

This study conducts a clinical evaluation of a secure, locally deployed, quantized large language model (LLM) for automating modified Rankin Scale (mRS) score extraction from unstructured neurosurgical notes. We retrospectively selected 103 authentic clinical letters (2007–2025) from aneurysm patients at a tertiary neurosurgical centre. To comply with data privacy constraints, an open-source reasoning LLM (Qwen3-32B) with 4-bit quantization was deployed entirely on-premises. The LLM extracted mRS scores using a zero-shot approach with custom logits processors to enforce strict JSON formatting. Performance was compared to a reference standard (attending neurosurgeons’ consensus) and parallel scoring by medical residents and students. The LLM achieved excellent agreement with the attending consensus (QWK 0.95), matching the reliability of medical students (QWK 0.95) and residents (QWK 0.93). Exact agreement was 75%, and agreement within ±1 mRS point was 96%. Bayesian analysis strongly supported statistical equivalence between the model and human raters. The computationally optimized LLM demonstrated human-level classification reliability without task-specific fine-tuning. This approach successfully addresses key patient data privacy barriers and the formatting inconsistencies typical of open-ended generative models. Securely deploying a general-purpose, quantized LLM provides a scalable pathway for extracting functional outcomes and supports FAIR-aligned data systems. Full article

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge–Fog–Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96–99.47% for accuracy and 99.08–99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments. Full article

National governments increasingly promote corporate sustainability through approaches ranging from rigid mandates to voluntary market-based compliance. State auditing may offer a middle-ground governance mechanism, but its role in improving Corporate Sustainability Performance (CSP) remains underexplored. China provides a distinctive setting: the China National Audit Office (CNAO), the country’s supreme audit institution under the State Council, audits state-owned enterprises (SOEs). Exploiting staggered CNAO interventions, we apply a staggered difference-in-differences design to 7883 firm-year observations of Chinese A-share-listed SOEs from 2009 to 2022. Government audit exposure is associated with a statistically significant but modest 0.118-point increase in CSP, measured using Huazheng (Sino-Securities) ESG ratings, equivalent to about 0.12 standard deviations. The result is robust to propensity score matching, parallel-trend and placebo tests, ESG-pillar decomposition, and external validation using green patent filings. Information disclosure quality and media attention strengthen the effects, indicating that transparency and public scrutiny amplify government oversight. Effects are more pronounced among non-heavily polluting industries, larger firms, firms with fewer financing constraints, and enterprises located in eastern China. These patterns suggest that audited SOEs respond as cost-benefit-sensitive market participants rather than passive state agents. These findings imply that structured governmental oversight can promote sustainability when complemented by transparency mechanisms and adequate firm-level capacity. Full article