Search Results (34,452)

Multi-asset portfolio optimization under non-stationary financial markets requires robust state representations across market phase transitions. This paper proposes distribution-robust graph representation learning for portfolio optimization (DR-GRL-PO), which learns asset-dependency graph representations as robust cross-asset structural priors for policy learning. DR-GRL-PO consists of a market-phase invariant graph contrastive encoding module (MPIGCE), a distribution-robust predictive coding module (DRPC), and a portfolio policy learning module (PPL). MPIGCE learns invariant structural priors from Spearman-based asset-dependency graphs, DRPC incorporates these priors into dual-scale predictive branches with invariant ranking consistency, and PPL integrates structural priors and predictive states for dynamic asset allocation. The model is evaluated on three separate datasets for portfolio construction, using daily data from CSI-300 (2011–2021), NASDAQ-100 (2011–2021), and Cryptocurrency (2017–2026) markets. The results show that DR-GRL-PO mainly improves wealth growth, annualized profitability, and risk-adjusted performance, while maintaining a certain degree of downside-risk control and a favorable upside–downside return balance. Its performance across separate market categories and market phases provides evidence of robustness under non-stationary market conditions. These findings indicate that robust cross-asset structural priors can support more reliable dynamic portfolio allocation. Full article

Background: Systemic inflammation plays a central role in determining postoperative outcomes in patients undergoing isolated coronary artery bypass grafting with cardiopulmonary bypass. Traditional inflammatory indices such as the neutrophil-to-lymphocyte ratio and the platelet-to-lymphocyte ratio have prognostic value; however, their dynamic behavior during cardiopulmonary bypass remains insufficiently characterized. More comprehensive indices, including the systemic immune-inflammation index and the systemic inflammatory response index, may help characterize early intraoperative inflammatory activity; however, their prognostic relevance should be regarded as exploratory and requires prospective validation. Methods: This retrospective nested case–control study included 245 patients who underwent isolated coronary artery bypass grafting, and intraoperative inflammatory indices during cardiopulmonary bypass were evaluated. Because of the nested case–control design, mortality cases were intentionally overrepresented to improve statistical power; therefore, the observed mortality rate does not reflect the true institutional mortality rate. Inflammatory indices (NLR, PLR, SII, and SIRI) were calculated at induction, at the 5th, 45th, and 90th minutes during cardiopulmonary bypass, and in the early postoperative period. Associations between these indices and in-hospital mortality were evaluated using univariate and multivariable logistic regression analyses. Predictive performance was assessed using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Results: The final enriched analytical sample consisted of 51 mortality cases and 194 randomly sampled surviving controls. During cardiopulmonary bypass, inflammatory indices, particularly at the 5th minute, were significantly higher in patients who experienced mortality (p < 0.001 for all major indices). SII demonstrated the strongest predictive performance at the 5th minute (AUC = 0.790), followed by SIRI (AUC = 0.765), PLR (AUC = 0.687), and NLR (AUC = 0.681). In multivariable analysis, SII and SIRI measured at the 5th minute remained independent predictors of mortality. The addition of 5th-minute SII to the limited study-specific clinical model, which included age, ejection fraction, and preoperative creatinine, improved exploratory discrimination for in-hospital mortality (with AUC increasing from 0.698 to 0.797). Conclusions: Early intraoperative assessment of inflammatory indices during cardiopulmonary bypass may provide additional prognostic information in patients undergoing coronary artery bypass grafting. Composite indices, particularly SII and SIRI, showed stronger exploratory discrimination than traditional inflammatory markers in this enriched analytical sample. However, these findings should be considered hypothesis-generating and require prospective external validation before use in perioperative risk stratification or clinical decision-making can be recommended. Full article

Thienopyrimidine derivatives are emerging as potent scaffolds for cholinesterase inhibition in Alzheimer’s disease therapy. In this work, a novel series of substituted thieno[2,3-d]pyrimidines was synthesized via Gewald’s reaction, followed by cyclization and functionalization through nucleophilic substitution and hydrazone formation. Structural confirmation was achieved using spectroscopic techniques, and biological evaluation was performed against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with donepezil and rivastigmine as reference drugs. Compound 4 emerged as the most potent and selective AChE inhibitor (IC₅₀ = 0.58 µM), while compound 7 also showed strong AChE inhibition (IC₅₀ = 0.63 µM). Notably, compound 9 exhibited superior BChE inhibition (IC₅₀ = 3.05 µM) compared to donepezil (IC₅₀ = 8.41 µM). Dual inhibitory activity was observed for compounds 5, 6, and 11, highlighting their multitarget potential. Molecular dynamics simulations (200 ns) and MM/GBSA binding free energy calculations provided mechanistic insights. Compound 4 showed the most favorable binding energy (ΔGbind = −59.16 kcal/mol), driven by hydrogen bonds with Tyr121 and Glu199 and π-π stacking with Trp83. Residue-level decomposition identified Tyr121, Trp83, Glu199, and Tyr338 as critical contributors to binding stability. Structure–activity relationship analysis confirmed that nitrogen-containing substituents and cyclic amino moieties enhance potency, whereas bulky aromatic groups reduce activity. These findings establish thieno[2,3-d]pyrimidine derivatives as promising candidates for the development of next-generation anti-Alzheimer agents. Full article

Federated learning (FL) has emerged as a promising paradigm for enabling distributed intelligence in Internet of Things (IoT) environments while preserving data privacy and reducing the need for centralized data collection. However, the practical deployment of FL in IoT scenarios remains challenging due to heterogeneous data distributions, unreliable communication conditions, and the presence of faulty or malicious edge devices that can disrupt collaborative training. These limitations can significantly degrade convergence stability and predictive performance, particularly in resource-constrained and intermittently connected networks. This paper proposes a simulation-driven trust-aware federated learning framework for robust intelligent IoT networks. The proposed approach incorporates a dynamic trust-based aggregation mechanism that adaptively weights client contributions based on the consistency of their local model updates with the global model state. In addition, a controlled IoT-oriented federated simulation environment is developed to emulate heterogeneous edge conditions, including non-independent and identically distributed (non-IID) data partitioning, adversarial model manipulation, and intermittent client connectivity caused by communication dropouts. Extensive multi-seed experiments were conducted on the UCI Human Activity Recognition (UCI HAR) dataset and complemented with an auxiliary CIFAR-10 convolutional neural network (CNN) validation scenario. The evaluation considered multiple adversarial settings, including sign-flip, Gaussian-noise, scaling, and label-flip attacks, as well as communication-dropout probabilities up to 50%. In contrast with the initial FedAvg-only evaluation, the revised experimental analysis includes comparisons with representative robust aggregation baselines, namely Median, Trimmed Mean, Krum, Multi-Krum, and an auxiliary Bulyan configuration. The experimental results demonstrate that the proposed Trust-FedAvg framework substantially improves robustness over conventional FedAvg and remains competitive with established robust aggregation strategies, particularly under directional model-manipulation attacks and intermittent-connectivity conditions. Under a 20% sign-flip attack on UCI HAR, the proposed method achieved a final test accuracy of 86.2%, whereas conventional FedAvg degraded to approximately 44.7%. Furthermore, under combined adversarial and intermittent-connectivity conditions with 50% communication dropout, Trust-FedAvg maintained a final accuracy of 57.0%, compared with 21.3% for FedAvg, 24.8% for Median, and 14.6% for Trimmed Mean. The additional experiments also show that Trust-FedAvg is not universally superior across all perturbation types: under severe Gaussian-noise attacks, coordinate-wise Median and Multi-Krum provided stronger robustness in some settings. Overall, the results suggest that trust-aware aggregation can improve robustness against unreliable or malicious simulated clients while preserving a relatively simple aggregation procedure. Runtime measurements further indicate that the proposed method introduces only limited round-level overhead compared with FedAvg, while remaining simpler than more complex Byzantine-resilient alternatives. Further validation with real IoT deployments, additional sensor datasets, asynchronous communication models, energy profiling, and communication-overhead measurements is required to fully assess deployment feasibility in real IoT environments. The proposed framework provides a practical, extensible basis for the design and evaluation of resilient AI-enabled IoT networks operating under controlled but practically relevant edge-learning constraints. Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

Quantum computing has emerged as a revolutionary technology in recent years, with wide-ranging applications across many fields. It provides a significant advantage in terms of exponential speedups, leading researchers to believe that classical computing cannot overcome this gap. However, its true potential has not yet been thoroughly investigated in statistics. In the present study, we incorporate quantum dynamics into the statistical estimation method and propose a quantum-based estimation approach, i.e., quantum maximum likelihood estimation. The proposed method leverages quantum principles and dynamics to estimate the unknown parameters of probability distributions. This study implements the proposed method to estimate the Laplace location parameter. Simulation studies and real-world analyses are performed to evaluate the performance of the QMLE estimate of the Laplace parameter compared to the MLE estimate. The validity of the QMLE estimate is also assessed through variance and convergence analyses. All the findings validate the potential computational advantages of the QMLE approach as a competitive and promising method for parameter estimation of the Laplace parameter. QMLE provides more accurate, precise, efficient, less uncertain, and better-fitting estimates than MLE. Overall, the results indicate that statistical estimation theory can be improved by incorporating quantum dynamics into the classical estimation process. Full article

Unmanned Aerial Vehicles (UAVs) have become critical platforms for missions such as surveillance, reconnaissance, and target tracking, which require real-time decision-making, reliable sensing, and efficient resource utilization. However, limited onboard computing capacity, energy constraints, variable terrain conditions, and situations where targets are partially or fully obscured limit the performance of traditional fixed-configuration sensing and tracking approaches. In this study, we propose a context-aware and adaptive UAV-based target detection and tracking framework that dynamically selects the most appropriate detection and tracking algorithm by jointly evaluating terrain characteristics and mission requirements. The proposed system includes a three-stage terrain analysis module supported by HSV color space filtering, Canny edge detection, Laplacian texture variance, and contrast-based features. In cases where color-based classification is insufficient, Random Forest-based classification is used to distinguish between vegetation, bare ground, and urban areas; the terrain classification model achieves approximately 90% accuracy during the training and testing process. In the target detection phase, a YOLOv11-based model was trained on a specialized tank dataset created from various sources and labeled in YOLO format, achieving an mAP50 performance of approximately 85%. In the tracking phase, single-object and multi-object tracking algorithms are selected via a scoring-based decision mechanism depending on the terrain type and mission scenario. Additionally, a hybrid anomaly detection mechanism that evaluates target loss, sudden bounding box changes, and view inconsistencies was integrated into the system, thereby enhancing tracking reliability and enabling the re-detection or algorithm switching process when necessary. Experimental results demonstrate that the proposed context-aware approach can reduce computational load while maintaining tracking robustness under various environmental conditions. These findings highlight that environmental awareness and adaptive algorithm selection can make significant contributions to autonomy, operational efficiency, and real-time reliability in UAV-based imaging systems. Full article

The proliferation of Internet of Things (IoT) devices operating at the network edge has created unprecedented demand for distributed machine learning capable of functioning under severe resource constraints. Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training across distributed nodes; however, its application to energy-constrained edge environments remains insufficiently characterized at the system level, particularly with respect to reproducible evaluation of resource consumption and communication efficiency. In this paper, we present EcoFL (Energy-Conscious Federated Learning), a modular, energy-aware benchmarking and orchestration framework for systematic evaluation of lightweight machine learning models under emulated edge hardware constraints. Rather than proposing a new federated optimization algorithm, EcoFL extends a standard FedAvg-based training pipeline with three principal components: (i) an energy-aware communication scheduler that dynamically adapts aggregation rounds and client participation based on per-node resource availability; (ii) a comprehensive system-level profiling pipeline capturing CPU utilization, RAM consumption, inference latency, communication overhead, and estimated computational energy consumption per training round; and (iii) a reproducible benchmarking methodology enabling fair comparison of centralized, standard federated (FedAvg), and energy-aware federated configurations. We evaluate five lightweight model families—Logistic Regression, Random Forest, XGBoost, Multilayer Perceptron, and Isolation Forest—under emulated Raspberry Pi 4 hardware constraints using an anomaly detection task on synthetic IoT sensor telemetry (50,000 samples, 12 features, Dirichlet non-IID partitioning). Experimental results across five independent seeds show that, within the evaluated benchmark setting, EcoFL reduces estimated federated training energy by 79.9–92.9% (mean 84.4%) relative to standard FedAvg through adaptive round termination (4–7 rounds versus 20 fixed rounds), while showing no statistically significant F1-score degradation for four of the five evaluated model families under the tested seed regime. Notably, EcoFL achieves a higher F1-score than FedAvg for Random Forest (+0.052), which we attribute to reduced overfitting resulting from earlier convergence under non-IID data distributions. The full EcoFL framework is released as open-source software to promote reproducibility in energy-aware federated learning research and to facilitate systematic investigation of the trade-offs between predictive performance, resource utilization, and communication overhead in resource-constrained edge environments. Full article

Almond processing byproducts are rich in bioactive polyphenols but severely underutilized due to inefficient conventional extraction methods. This study presents the first systematic optimization of an integrated ultrasound-assisted enzymatic extraction and AB-8 macroporous resin purification process for almond hull polyphenols, addressing the limitations of low yield, high impurity content, and bioactivity loss in traditional approaches. Extraction parameters were optimized via single-factor experiments combined with Box–Behnken response surface methodology, while purification conditions were refined through static and dynamic adsorption–desorption tests. Structural characterization and antioxidant evaluation were performed using Ultraviolet-Visible Spectroscopy (UV-Vis), Fourier Transform Infrared Spectroscopy (FT-IR), Scanning Electron Microscopy (SEM), 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and Ferric Reducing Antioxidant Power (FRAP) assays. Under optimal conditions, the polyphenol yield reached 23.67 mg/g. After purification, polyphenol purity increased 5.88-fold, flavonoid purity improved 4.62-fold, and DPPH/FRAP antioxidant activities were enhanced 5.0-fold and 6.5-fold, respectively. Purified polyphenols retained intact phenolic structures and exhibited a loose porous microstructure. This green process provides a technical basis for high-value utilization of almond hulls. Limitations include lack of polyphenol monomer identification, in vivo efficacy validation and industrial economic feasibility assessment. Full article

This study examines the characteristics of Group Multi-Attribute Decision Making (GMADM), including highly ambiguous information, divergent expert opinions, and bounded rationality among decision-makers. From the perspective of symmetry modeling and bias control, we propose an adaptive decision-making framework based on Spherical Fuzzy Sets (SFS). First, a spherical fuzzy quantification system for online reviews is constructed to map multi-source asymmetric information within reviews to Spherical Fuzzy Numbers. Second, an adaptive expert weighting mechanism is developed that integrates individual expert performance with the level of group consensus, dynamically adjusting weights to suppress the asymmetric interference of outlier opinions. Subsequently, we design the Credibility-based Spherical Weighted Arithmetic Mean (CSWAM) to preserve the dominance of expert judgments in a nonlinear manner and construct the Spherical Fuzzy Score function with Adaptive Hesitation Penalty (HP-SC) to ensure robustness and non-negativity in the defuzzification process. Furthermore, we extend DEMATEL and TODIM to the SFS environment, constructing a comprehensive evaluation model that captures causal relationships among attributes and asymmetric information, such as decision-makers’ loss aversion. Finally, empirical results from online movie review usefulness rankings demonstrate that this model can accurately identify and mitigate asymmetric information biases while maintaining decision symmetry equilibrium and exhibiting higher ranking stability. Full article

To address the issues of vibration blind spots and the “black box” of quality control in the vibration process of precast beams caused by dense reinforcement zones and the arbitrariness of manual operation, this paper proposes the development of a closed-loop intelligent vibration system. This system integrates intelligent equipment, evaluation models, adaptive algorithms, and digital twin feedback control. By combining drum-type vibration robots for large-scale operations with wearable sensing devices for complex dead corners, full coverage of the operation area is achieved. Second, based on the coupling mechanism of mechanical-electrical-hydraulic multi-physics fields, a quantitative evaluation model for vibration compactness is established. Furthermore, a hierarchical adaptive control strategy based on feedback of the rheological state is designed. By fusing real-time positioning data with vibration energy indices, process parameters such as vibration frequency and duration are dynamically optimized. Finally, through the developed digital twin feedback control platform, a vibration energy cloud map reflecting the internal quality can be generated in real time, thereby achieving process visualization and early warning of defects. Results show that the system can accurately detect and resolve missed and insufficient vibration issues. Construction activities are thus managed via data rather than subjective empirical judgment. Full article

The fracture–cave carbonate reservoir in Unit S91 of the Tahe Oilfield is jointly controlled by strike-slip fault activity, karstification, and later-stage fracture development, resulting in reservoir spaces characterized by strong heterogeneity, strong discreteness, and multi-scale superimposition. The inter-well connectivity of this type of reservoir is not governed by the size of a single fracture–cave body or local fracture density, but rather by the spatial configuration among the main controlling fault, the associated fracture network, and the fracture–cave reservoir bodies. As the reservoir enters the middle–high-water-cut development stage, the production differential between dominant connecting channels and weakly connected fracture–cave bodies further enlarges, leading to marked heterogeneity in the remaining oil distribution. Integrating post-stack seismic data, fracture prediction, RGB attribute fusion, production performance, and numerical simulation data, this paper constructs a main fault-controlled fracture–vug coupling probability (MFC-FVCP) model under the constraint of the main controlling fault. Unlike conventional multi-attribute fusion methods that mainly enhance seismic anomaly visualization, the MFC-FVCP model transforms the main fault constraint, fracture connectivity, and fracture–cave reservoir-body effectiveness into a unified coupling probability. The model uses three core components—the main fault response field, the fracture attribute response field, and the fracture–cave reservoir body response field—to characterize the fault-control effect, fracture-network continuity, and effective reservoir-body response, respectively. By evaluating the coupling probability, the inter-well connectivity potential is assessed, the dominant connectivity areas where fractures and fracture–cave bodies synergistically develop under the constraint of the main controlling fault are identified, and potential remaining oil targets are clarified. The predicted connectivity pattern was further constrained by production performance, nitrogen injection response, and staged oil saturation simulation, which improves the reliability of remaining oil enrichment prediction. The results show that the T74 layer is the dominant development interval of fracture–cave reservoir bodies in Unit S91. These fracture–cave bodies are mainly distributed along the main controlling fault and associated fracture zones in beaded, chain-like, and banded patterns, exhibiting distinct fault-and-fracture control characteristics. Potential point A near well TK858XCH features both good reservoir physical properties and insufficient sweep efficiency, making it a key target for subsequent injection–production adjustment and remaining oil tapping. The MFC-FVCP model can incorporate static seismic responses, fracture–cave spatial structures, and dynamic development responses into a unified evaluation framework, providing a quantitative basis for characterizing inter-well connectivity and identifying remaining oil enrichment areas in fracture–cave carbonate reservoirs. Full article

Accurate characterization of flood dynamics in large river–lake systems remains challenging due to the difficulty of simultaneously capturing inundation extent and water surface elevation (WSE) variations under rapidly changing hydrological conditions. This study develops an integrated Synthetic Aperture Radar (SAR) and wide-swath interferometric altimetry framework to reconstruct the spatiotemporal evolution and storage dynamics of the 2024 flood event in the East Dongting Lake system, China. Sentinel-1 SAR imagery is utilized to derive high-resolution inundation extent, while the Surface Water and Ocean Topography (SWOT) mission, equipped with the Ka-band Radar Interferometer (KaRIn), provides two-dimensional WSE observations. To improve SAR-based flood extraction in heterogeneous floodplain environments, an Adaptive Spatially-Constrained Fuzzy C-Means (AS-FCM) algorithm is proposed by incorporating adaptive spatial regularization and structure-aware neighborhood weighting. Quantitative evaluation demonstrates that the proposed method achieves the highest performance among the evaluated conventional approaches, with an Overall Accuracy of 93.6%, an Intersection over Union of 0.89, and a Kappa coefficient of 0.87. The multi-temporal inundation sequence reveals a distinct flood evolution pattern characterized by rapid expansion during the rising stage and gradual recession during the post-peak period. SWOT-derived WSE observations exhibit strong agreement with synchronous in situ measurements after bias adjustment, with a correlation coefficient of 0.988. By integrating SAR-derived inundation extent with temporally matched water-level observations constrained by bias-adjusted SWOT and in situ gauge data, an empirical WSE–area relationship ($R^2=0.937$) is established to reconstruct daily flood dynamics and estimate cumulative water storage variation. The results indicate that the East Dongting Lake floodplain played an important buffering role during the 2024 flood event, with cumulative storage variation reaching approximately $10.7{\mathrm{km}}^3$ during the peak stage. Overall, the proposed framework demonstrates strong potential for flood monitoring and hydrological storage assessment in complex river–lake systems. Full article

To address the insufficient low-temperature performance of asphalt modified with direct coal liquefaction residue (DCLR), this study proposed a composite modification strategy based on an extraction–blending process using styrene–butadiene–styrene (SBS) and crumb rubber (CR). The high- and low-temperature rheological properties, phase morphology, and functional-group characteristics of DCLR-blended asphalt with different formulations were systematically evaluated using a dynamic shear rheometer (DSR), a bending beam rheometer (BBR), fluorescence microscopy (FM), and Fourier transform infrared spectroscopy (FTIR). The results demonstrate that the combined addition of SBS and crumb rubber significantly enhances the high-temperature stability and elastic response of the asphalt. Specifically, formulation 5# (8 wt.% SBS and 10 wt.% CR) maintained a rutting factor of 1.007 kPa at 82 °C, indicating superior high-temperature rutting resistance. Meanwhile, this formulation satisfied the Superpave low-temperature requirements at −18 °C, achieving a balanced improvement in both high- and low-temperature performance. Microstructural analysis suggests that an appropriate SBS/CR ratio contributes to the formation of a relatively continuous and uniformly distributed polymer-rich phase, whereas excessive modifier contents may lead to rubber agglomeration and phase-structure imbalance. FTIR results showed that the characteristic absorption peaks of the modified binders were generally consistent with those of the base asphalt, and no obvious new absorption bands were observed. This indicates that the extraction–blending process mainly involved physical blending, swelling, and phase interaction rather than the formation of new covalent functional groups. This study provides a technical reference for the high-value utilization of DCLR and the development of high-performance modified asphalt. Full article

Institutional governance has traditionally been analyzed under the assumption that the space of potential violations is finite, enumerable, and progressively constrainable through rule refinement and calibrated enforcement. The rapid integration of large language models into strategic and documentary decision-making challenges this premise by transforming feasible deviation spaces from bounded sets into generative manifolds. This paper develops a formal simulation framework for examining institutional stability under algorithmically amplified strategic exploration. Regulatory rules are modeled as a constraint manifold characterized by effective dimensionality, while generative systems expand the behavioral strategy space through semantic recombination under detection and sanction constraints. Stability is defined through a minimum deterrence margin evaluated across the generatively reachable domain rather than only through historical violation catalogs. The study uses a 2014–2023 regulatory and violation corpus to initialize and calibrate the simulation and to conduct a limited historical hold-out check; the 250,000 LLM-generated scenarios are treated as synthetic stress-test proposals rather than observed violations. The computational specification reports the generator checkpoint, embedding model, decoding parameters, prompt templates, random seeds, filtering rules, and label partitions used in the simulation. The model introduces a dimensional dominance principle: systemic vulnerability may emerge in the simulation when the effective dimensionality of generative strategic search expands faster than the independent constraint dimensionality of the rule system. Under the reported baseline setting, the synthetic simulations show a pipeline-specific dimensional crossover, convergence limits in rule-consistency classification, and a nonlinear detection–sanction response surface. These outputs are interpreted as diagnostics of the stated computational pipeline, not as universal empirical laws about real institutions. The power-grid component is delimited accordingly: the paper does not simulate physical grid operation, power flow, dispatch, or relay-protection dynamics; it interprets the model at the documentary governance layer of power-grid enterprises, including procurement, construction supervision, maintenance records, dispatch-related documentation, customer-service reporting, and internal audit. The framework therefore provides a reproducible and cautiously delimited basis for analyzing text-mediated institutional resilience in the age of generative intelligence. Full article