Search Results (230)

With the large-scale integration of electric vehicles (EVs), distributed charging scheduling has become a key enabler for coordinated charging management. However, its reliance on information exchange makes it susceptible to cyberattacks, including False Data Injection (FDI), Denial of Service (DoS), and replay attacks. Such attacks may compromise privacy and corrupt or interrupt communication, leading to incorrect consensus prices and degraded scheduling performance. To mitigate these threats, this paper proposes a distributed resilient charging scheduling strategy. Specifically, anomalous nodes are detected through neighbor-based observations, while a belief-degree-based trust mechanism is employed to isolate low-trust nodes and suppress attack propagation. In addition, an individual price resetting mechanism is developed to restore convergence to the optimal price of the remaining EVs following node isolation. Simulations on communication networks with 5 to 100 EVs show that, under all three attacks, the compromised node is detected at the second and isolated at the third consensus iteration after attack onset, no healthy node is falsely isolated, and the remaining fleet converges to the optimum of the reduced scheduling problem with a price deviation below $8.2\times {10}^{-3}$. A buffered detection envelope extends these guarantees to asynchronous communication, heterogeneous time-varying delays, packet losses, and intermittent attacks: in 245 randomized stress runs on 5-to-100-EV networks, every attacker is isolated, no healthy node is isolated outside the harshest composite scenario, and the final price deviation remains below $1.1\times {10}^{-2}$. Extensive simulations under representative cyberattack scenarios verify the effectiveness and robustness of the proposed strategy within the stated assumptions. Full article

Agentic large language model (LLM) networks are increasingly used in safety-critical settings where autonomous agents invoke tools, exchange context, and coordinate decisions. Prompt-injection attacks remain a significant threat to these multi-agent pipelines because they can compromise data flows between agents, bypass instruction hierarchies, and corrupt output integrity. Although defenses against injected prompts and mechanisms for cryptographically verifying model-related computations have been studied independently, no common framework unifies these complementary security perspectives in a protocol suitable for real-time agentic deployments. From the perspective of symmetry, secure inter-agent communication requires the preservation of an invariant integrity relationship between a message at its source and the corresponding message accepted at its destination. A benign communication path therefore exhibits a form of integrity symmetry, whereas prompt injection or message manipulation creates an asymmetric state in which the received payload, its semantic effect, or the receiving model’s internal activation pattern deviates from the trusted reference state. In this paper, we propose SecurePrompt-IntegrityNet (SPI-Net), a prompt-injection-resilient data integrity verification protocol that combines cryptographic attestation with anomaly-aware activation monitoring. SPI-Net provides three closely related mechanisms: a Merkle-tree-based commitment system that verifies the provenance and integrity of data payloads exchanged between agents; a layer-wise Mahalanobis-scoring Activation Anomaly Detector (AAD) that identifies distributional shifts in the intermediate representations of LLMs; and a Trust Propagation Consensus (TPC) mechanism that combines cryptographic and behavioral evidence into per-payload integrity verdicts. In this formulation, the Cryptographic Attestation Module (CAM) tests whether message-level structural symmetry is preserved between the sender and receiver, whereas the AAD detects behavioral symmetry breaking in activation space. Experiments on three multi-agent benchmarks under five adaptive attack strategies show that SPI-Net achieves a 96.8% detection rate with a 1.7% false positive rate, reduces the attack success rate by 94.3% relative to undefended baselines, verifies data integrity with 99.2% accuracy, and introduces only 38 ms of median per-message latency. These results demonstrate that jointly preserving cryptographic integrity symmetry and identifying activation-level asymmetry provides substantially stronger prompt-injection resilience than either verification mechanism alone. Full article

Water ecosystems face increasing threats from pollution, climate change, and extreme hydrological events, while conventional monitoring systems often provide limited adaptability and spatial coverage. This paper proposes a 6G-enabled smart watershed monitoring framework integrating Flying Ad Hoc Networks (FANETs), Internet of Things (IoT) sensors, deep learning, and Multi-Agent Deep Reinforcement Learning (MADRL). Cooperative unmanned Aerial Vehicles (UAVs) interact with terrestrial IoT nodes through a simulated 5G/6G communication environment, while deep learning models support water-quality prediction and ecological-risk assessment. The MADRL framework enables UAV agents to collaboratively optimize sensing coverage, data collection, energy consumption, and pollution-event response under dynamic environmental conditions. The framework is evaluated in a Python-based simulation environment using five UAVs and distributed IoT sensing nodes under normal and pollution-affected watershed scenarios with communication impairments and environmental disturbances. Performance is compared with centralized static monitoring, rule-based UAV patrol, and single-agent reinforcement learning using spatial coverage, pollution-detection latency, prediction accuracy, false-alarm rate, energy consumption, and communication metrics. Results show approximately 40% higher spatial coverage, 60% faster pollution-event detection, 15% higher water-quality prediction accuracy, and 35% lower false-alarm rates than the considered baselines. The results also indicate improved communication resilience and energy-aware UAV coordination. As the evaluation is simulation-based, these findings demonstrate computational feasibility and comparative effectiveness under the specified assumptions rather than physical deployment. Real-world UAV testbed and watershed validation remain important future directions. Full article

This article reviews the evolution and current status of using blockchain technology to improve Smart Grid security and trust. It also summarizes the main challenges faced by blockchain-based Smart Grid systems. The survey provides a clear explanation of the various risks that threaten the blockchain-based Smart Grid and explores how AI technologies can support intrusion detection systems in identifying anomalies. It further highlights the importance of emerging quantum-computing risks and the challenges of adopting post-quantum cryptography in resource-constrained smart meters. The current AI evidence is concentrated on offline detection of false data and traffic anomalies, while operational validation, uncertainty calibration, adversarial robustness, and deployment-cost reporting remain limited, partly due to the lack of publicly available real-time BSG datasets and the complexity of realistic Smart Grid simulations. Further, post-quantum migration is constrained by the memory, bandwidth, latency, and energy budgets of long-lived smart-meter hardware. The abstract also identifies end-to-end evaluation of hybrid classical/post-quantum signatures as a priority. These insights support the development of more secure and resilient Smart Grid systems. By addressing these challenges and implementing robust countermeasures, this article aims to enhance the overall security of Smart Grids and promote their long-term resilience. Full article

Industrial water treatment plants are increasingly dependent on cyber–physical systems (CPS) and automated control processes for their operational safety and efficiency. However, the embedding of digital control networks exposes these critical infrastructures to sophisticated cyber–physical attacks, including malicious tampering with chemical dosing units and physical actuators. This paper proposes a robust, AI-enhanced anomaly detection framework designed to identify multi-stage malicious activities in water treatment systems using real-world industrial datasets. The proposed system is developed and validated on the Secure Water Treatment (SWaT) dataset, which contains multivariate sensor and actuator time-series data collected from a fully operational physical testbed under both normal operations and targeted cyber–physical attacks. First, high-frequency sensor noise is filtered, and cross-channel measurement reliability is maximized using a Kalman filter-based sensor fusion module. Subsequently, the fused-state vector is analyzed using an unsupervised Isolation Forest algorithm optimized for high-dimensional boundary isolation. To eliminate false negatives caused by stealthy, low-amplitude data injections that bypass purely statistical models, a deterministic, rule-based verification layer derived from physical process control logic is integrated. By integrating a discrete linear Kalman filter with an unsupervised Isolation Forest and deterministic physical rules, the framework effectively suppresses high-frequency sensor noise, achieving a 67.8% reduction in root mean square error (RMSE), while maintaining high detection accuracy across complex industrial attack scenarios. Experimental results demonstrate that the proposed hybrid framework yields superior detection capability, achieving a Precision of ≈95%, a Recall of ≈93%, a scenario-level F1-score of $94.1\%$ (alongside a sample-level F1-score of $21.5\%$) and an edge inference latency of $0.6 \mathrm{ms}$, effectively demonstrating its suitability for deployment within simulated real-time industrial edge computing environments. The findings further confirm that combining statistical machine learning, state-space sensor fusion, and invariant physical process logic provides a resilient defense paradigm for securing critical industrial infrastructure against modern cyber–physical threats. Full article

The coupling of power and natural gas infrastructures introduces gas-side constraints and cyberattack risks into load frequency control (LFC). This paper proposes an auxiliary Kalman filter (AKF)-based tube-based MPC (TMPC) framework for gas–electricity coupling systems under gas pressure false data injection attacks (FDIAs). A pressure-dependent gas turbine (GT) power limit is incorporated into the frequency control constraint, and an auxiliary pressure model is identified from attack-free data as a virtual pressure sensor. Residuals, normalized innovation squared statistics (NIS), and cumulative sum (CUSUM) statistics are used for attack detection, while compromised pressure measurements are reconstructed using the AKF estimates. Simulations on a two-area power system coupled with an 11-node gas network show that under attack-free operations, the integral absolute error (IAE) values of TMPC, conventional MPC, and PI control are 0.7040, 1.9626, and 2.9834 Hz·s, respectively. Thus, TMPC reduces the accumulated frequency deviation by approximately 64% and 76%, compared with conventional MPC and PI control, respectively. Under FDIAs, the IAE decreases from 1.1645 to 0.7039 Hz·s after AKF-based pressure reconstruction, corresponding to an approximately 40% reduction. Meanwhile, the mean absolute error (MAE) of gas pressure reconstruction decreases from 0.1714 to 0.0157 bar, corresponding to an approximately 91% reduction in pressure reconstruction error. Compared with the denoising autoencoder (DAE) and graph signal recovery approaches, the proposed method achieves the highest detection rate of 98.39%, effectively limiting FDIA propagation to GT constraints and frequency regulation. Full article

Abiotic stresses severely limit agricultural productivity and have stimulated growing interest in microalgae as sources of stress-resilient traits and biostimulants. However, comparative genomic assessments integrating multiple stress-associated gene families across chlorophyte genome assemblies are limited. Here, we analyzed 19 chlorophyte genome assemblies to investigate the distribution and copy-number variation of 11 gene families associated with antioxidant defense, osmoprotection, and carotenoid biosynthesis. Candidate genes were identified using standardized InterPro annotations and manually curated to ensure consistent gene copy-number estimation. Hierarchical clustering, principal component analysis (PCA), descriptive statistics, and Pearson’s correlation analysis were performed to characterize gene-family copy-number patterns and multivariate similarities among genome assemblies. Multiple tests in the correlation analysis were controlled using the Benjamini–Hochberg false discovery rate procedure. The total functional family assignments ranged from 28 to 51 across the analyzed genome assemblies. Thioredoxin (TRX) exhibited the highest mean copy number (17.26 copies per genome) and the lowest coefficient of variation (15.43%), whereas catalase (CAT) showed the greatest variability (CV = 59.28%). The first two principal components explained 50.18% of the total variation, with PC1 accounting for 30.86% and PC2 for 19.32%, and differentiated genome assemblies according to their stress-associated gene copy-number profiles. Several moderate-to-strong pairwise correlations were observed, but none were significant after FDR correction. Overall, this study provides a curated comparative genomic framework for identifying stress-associated gene-family copy-number patterns across chlorophyte genome assemblies and generating testable hypotheses for subsequent functional studies. Full article

Background/Objectives: ISO-based quality-management systems may be associated with organizational processes relevant to patient safety, but evidence regarding nurses’ safety-culture perceptions, resilience, and job satisfaction remains limited. This study compared nurses working in ISO-certified and non-ISO-certified hospital departments. Methods: This cross-sectional study included 263 nurses from 29 departments (11 ISO-certified and 18 non-ISO-certified) in one tertiary hospital in Northern Greece. Patient safety culture was assessed using the 10 dimensions of the Hospital Survey on Patient Safety Culture 2.0, together with the Brief Resilience Scale and Job Satisfaction Index. Unadjusted comparisons and covariate-adjusted linear regression models with heteroscedasticity-robust standard errors were performed, with Benjamini–Hochberg false-discovery-rate correction. Results: In unadjusted analyses, nurses working in ISO-certified departments reported higher Staffing and Work Pace (MD = 0.222, 95% CI [0.094, 0.351], g = 0.40) and Reporting Patient Safety Events (MD = 0.427, 95% CI [0.134, 0.719], g = 0.37), but lower Handoffs and Information Exchange (MD = −0.295, 95% CI [−0.458, −0.131], g = −0.45); all three differences remained significant after false-discovery-rate correction. Resilience and job satisfaction did not differ in unadjusted analyses. After adjustment and correction, working in an ISO-certified department was associated with higher Staffing and Work Pace, Supervisor/Manager/Clinical Leader Support, and job satisfaction, and with lower Hospital Management Support and Handoffs and Information Exchange; no association with resilience was observed. Conclusions: Working in ISO-certified departments showed selective, modest, and mixed nurse-reported associations. Because certification was a department-level characteristic and within-department clustering could not be modeled, the findings should be interpreted as exploratory rather than as estimates of a department-level certification effect. Full article

Marine fishery system resilience supports ecological and productive functions under interacting environmental and socioeconomic pressures. We assessed resilience and forecastability using 117 region–year observations from nine Chinese coastal provincial-level regions during 2011–2023. A fixed-reference Resistance–Adaptability–Recovery index combined equal and entropy weights calibrated on 2011–2018 data. We evaluated autocorrelation-adjusted trends, measurement sensitivity, regional score distributions, conditional associations using geographically and temporally weighted regression (GTWR), and rolling-origin forecast performance. The mean index increased from 0.307 in 2011 to 0.426 in 2023, and eight regions retained significant monotonic increases after false-discovery-rate correction. Alternative weighting, calibration, indicator-deletion, and aggregation specifications preserved the positive temporal direction while producing moderate rank variation. GTWR yielded the lowest in-sample AICc among the three regression specifications, supporting a spatiotemporally varying representation of conditional associations. Under rolling-origin testing, particle swarm optimization-tuned support vector regression produced an RMSE of 0.0318, whereas persistence achieved the lowest RMSE (0.0246) and lower region-level RMSE in seven of nine regions. Accordingly, the 2024–2030 results were reported as conditional scenarios. Eight regional specification ranges crossed zero; Guangdong remained positive across the nine specified trend-window and weighting combinations. The framework improves temporal comparability and separates historical assessment, spatial association analysis, and forecast evaluation. Full article

The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities. This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment of contemporary machine-learning approaches and recent integrated smart-grid research. Its architecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundation for coordinating complementary capabilities required for resilient anomaly management. Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article

Open hourly meter data are often used to infer how buildings respond to extreme heat, but the interpretation can depend on the analytical baseline, institutional calendar, and meter configuration. We examined 60 education and office buildings in the Building Data Genome Project 2 using repeated whole-week validation, temperature-matched placebo tests, 13 academic-calendar definitions, and same-building, common-hour carrier comparisons. A leakage-resistant change-point model that incorporated a previous-day, load-derived operating-regime proxy reduced blocked-validation error relative to a conventional model in 58 of 60 buildings; temperature binning provided no additional predictive benefit after regime control. Recovery-related signals were uncommon: rebound was significant in 2 of 49 eligible buildings, load-shape disruption in 1 of 49, and the 72 h duration diagnostic in 3 of 60. At Fox, the BASE calendar supported term and break comparisons through 39–42 °C, but neither building-use group differed significantly after false-discovery-rate adjustment; estimates above 42 °C were inconclusive. In the matched sample of 13 education and 10 office buildings, no electricity-only, chilled-water-only, or weighted specification provided robust evidence of a between-group difference. Across 99 combinations of carrier weight, normalization, and temperature bin, every 95% interval included zero. Reliable campus benchmarking therefore requires out-of-sample baseline assessment, explicit calendar sensitivity, common-hour meter matching, and carrier-specific uncertainty reporting before building-use rankings or resilience-related interpretations are made. Full article

Background/Objectives: The physiological adaptations required to sustain elite football performance are influenced by both genetic background and dynamic biochemical responses, although their interaction across a full competitive season remains insufficiently characterized. This study aimed to examine the association between polygenic profiles and longitudinal biochemical adaptations in professional football players. Methods: Forty male professional football players competing in the Spanish league were monitored across two consecutive seasons. Blood samples were collected at six time points representing different phases of the competitive cycle. Biomarkers related to muscle metabolism, iron status, and hepatic function were analyzed. Polygenic profiles were calculated using Total Genotype Scores (TGS) for muscle performance, hepatic resilience, and metabolic efficiency. Associations were initially explored using Pearson correlations and subsequently evaluated using linear mixed-effects models accounting for repeated measurements within subjects. Results: Exploratory correlation analyses identified several associations between polygenic profiles and biochemical markers. Muscle performance TGS was inversely associated with serum iron (r = −0.36, p = 0.017) and positively associated with CK (r = 0.32, p = 0.041), Hb (r = 0.29, p = 0.046), and Hct (r = 0.33, p = 0.024). Hepatic resilience TGS showed inverse associations with ALT (r = −0.39, p = 0.012), urea (r = −0.51, p = 0.011), and BUN (r = −0.51, p = 0.011). Metabolic efficiency TGS was negatively associated with AST (r = −0.43, p = 0.044), ALT (r = −0.33, p = 0.025), and GGT across multiple time points (p = 0.001–0.013). However, although several nominal associations emerged in linear mixed-effects models accounting for repeated measurements, none remained statistically significant after false discovery rate correction. These findings should therefore be interpreted as exploratory and hypothesis-generating. Conclusions: Polygenic profiles may be associated with inter-individual variability in biochemical adaptations throughout a competitive season. These findings suggest the integration of genomic and biochemical data in precision athlete monitoring, while highlighting causal relationships and predictive applications require further investigation. Full article

This paper studies resilient safe distributed optimization with practical prescribed-time performance for fractional-order multi-agent systems under false data injection attacks. Caputo dynamics are used to represent memory-dependent agent behavior. A local trimming mechanism is combined with gradient feedback, auxiliary coordination, a bounded time-varying gain, and a barrier-type safety correction. In contrast to an ideal singular prescribed-time design, the proposed bounded gain drives the normal-agent errors into an explicitly characterized neighborhood of the global optimizer at the assigned time, provided that a verifiable gain matrix is positive definite. Explicit residual bounds are derived in terms of the attack amplitudes, the trimming level, and the retained-neighbor redundancy. A fractional extremum argument is further used to establish forward invariance of the safe interval. The analysis proves boundedness of the physically relevant closed-loop signals, safety preservation, practical prescribed-time convergence, and robustness against bounded disturbances. Simulations on a sparse six-agent network demonstrate the roles of resilient information processing and barrier correction without requiring all-to-all communication. Full article

The high rate of growth of misinformation on decentralized platforms causes a risk to public confidence and the integrity of decisions and requires a system of verification that is not only accurate but can be causally informed and interpreted via proxy causal metrics, reliable, and privacy-safe as well. The state-of-the-art federated learning (FL)-based fact-verification models mainly use correlation-driven patterns and do not provide ways to deal with causal reasoning, measuring formal reliability, or being resilient to Byzantine adversaries. This paper proposes a unified framework called CORE-FACT (causal optimization and reliability-enhanced fact-tracking) that can be used to conduct interpretable and robust misinformation detection in a distributed environment by combining causally informed optimization with reliability-weighted federated optimization. The proposed three-tier architecture includes: (1) a causal graph construction module, where variational attention is utilized to learn directed relationships of claims and evidence; (2) a reliability-weighted federated optimization module, where Byzantine-resilient aggregation (adaptive trust scoring) is achieved; and (3) an adaptive fact-tracking module, which is used to achieve fusion of multi-source evidence by combining hierarchical consistency verification with knowledge-graph embeddings. Empirical testing on the LIAR and FEVER datasets shows that CORE-FACT achieves 94.7% and 96.3% accuracy, respectively, outperforming state-of-the-art baselines by 3.5 to 4.8 percentage points in accuracy, with 17% lower latency than GEAR and 31% lower latency than DAGNN, and 98.2% robustness against 30% Byzantine attacks. Under differential-privacy guarantees verified at ε = 10.96, δ = 10⁻⁵ (Renyi DP composition, empirical membership inference validation committed for revision), CORE-FACT achieves a 31% reduction in false positives through explicit causally informed reasoning. These findings make CORE-FACT a scalable and interpretable framework that consolidates causal optimization, trustworthiness evaluation, and secure aggregation for next-generation federated misinformation detection. Full article