Search Results (499)

Background and Objectives: Muscle symptoms are the most visible adverse event attributed to statins, but terminology is often imprecise. Most patients report myalgia or nonspecific aches, whereas objective myopathy, inflammatory or necrotizing myositis, rhabdomyolysis, and anti-HMGCR immune-mediated necrotizing myopathy are uncommon and clinically distinct entities. To provide a clinically oriented narrative synthesis of statin-associated muscle symptoms (SAMS) and severe statin-associated myotoxicity, and to propose a practical prevention, evaluation, and management algorithm. The classification of muscle events is used to standardize terminology and avoid diagnostic confusion, not to create a new formal taxonomy. Materials and Methods: A clinically oriented narrative review was performed using PubMed, Google Scholar, and major society documents published from January 2021 to April 2026. Eligible sources addressed SAMS, statin myopathy/myositis, rhabdomyolysis, anti-HMGCR immune-mediated necrotizing myopathy, nocebo/drucebo effects, pharmacogenetics, drug interactions, diagnosis, or management. The final evidence set comprised 55 verifiable sources, including blinded randomized or n-of-1/crossover evidence; meta-analyses; clinical statements and reviews; pharmacovigilance analyses; pharmacogenetic guidance; mechanism-focused reviews; anti-HMGCR series; and lipid-lowering guideline/treatment studies. Because the review was narrative, no pooled estimate or formal PRISMA screening log was generated. Results: Blinded evidence indicates only a small absolute excess of muscle pain with statins, concentrated mainly in the first year of therapy, and that most muscle symptoms reported during statin therapy are not pharmacologically caused by the statin. N-of-1 and crossover trials show that symptom intensity is often similar during statin and placebo periods, consistent with an important nocebo/drucebo contribution. Severe muscle toxicity can nevertheless occur, especially when systemic statin exposure is increased by a high dose, interacting drugs, frailty, renal or hepatic impairment, hypothyroidism, transporter or metabolic genotypes, or intense unaccustomed exercise. Statin choice matters chiefly through dose, pharmacokinetics, and interaction burden. Conclusions: SAMS are common as reported clinical problems, but confirmed statin-caused muscle injury is substantially less frequent than routine clinical attribution suggests. Permanent discontinuation should be reserved for carefully assessed cases. A structured approach—baseline risk assessment, selective CK measurement, exclusion of alternative causes, correction of modifiable risks, dechallenge/rechallenge, statin switching, dose reduction, and combination with non-statin therapy—preserves cardiovascular benefit while protecting the rare patient with genuine toxicity. Full article

(1) Background: Coronary artery disease (CAD) frequently coexists with thoracic aortic disease and may increase the risk of adverse outcomes after thoracic endovascular aortic repair (TEVAR). Whether routine preoperative coronary angiography (CAG) improves outcomes remains unclear. (2) Methods: We retrospectively analyzed 177 patients undergoing elective TEVAR between 2015 and 2025 with a median follow-up of 4.9 years. Two analyses were performed: patients who underwent preoperative CAG versus those who did not, and patients with versus without CAD. Survival was assessed using Kaplan–Meier analysis and overlap-weighted Cox regression. (3) Results: Preoperative CAG was performed in 94 patients (53.1%) and identified newly diagnosed or progressive CAD in 42 (44.7%). Overall, 24 patients (13.6%) underwent coronary revascularization before TEVAR. Patients with CAD were older and had a greater comorbidity burden. Despite these differences, preoperative CAG was not associated with differences in in-hospital mortality (2.1% vs. 6.0%, p = 0.159), major adverse cardiovascular events (11.3% vs. 9.0%, p = 0.754), or long-term survival (log-rank p = 0.10). Patients with CAD showed higher unadjusted long-term mortality than those without CAD (31.7% vs. 17.5%; log-rank p = 0.003). However, after overlap weighting, CAD was no longer significantly associated with mortality (adjusted HR 1.4, 95% CI 0.71–2.8). Among patients with angiographically verified coronary disease, preoperative revascularization before TEVAR was not associated with improved long-term survival (HR 2.20, 95% CI 0.69–6.98). (4) Conclusions: Preoperative CAG detects clinically relevant, often unrecognized CAD in a substantial proportion of TEVAR candidates and enables revascularization before surgery. Despite a higher coronary burden, patients who underwent CAG had outcomes comparable to those who did not, and the crude long-term survival disadvantage of CAD was largely explained by the accompanying systemic atherosclerotic burden. Routine preoperative coronary assessment appears justified in elective TEVAR. Full article

Lactobacilli species have emerged as a focal point in food microbiology due to their core probiotic properties, including the regulation of intestinal homeostasis and the enhancement of immunity. This study focuses on Lacticaseibacillus rhamnosus MG0718 (hereinafter referred to as MG0718), employing a combined approach of phenotypic evaluation and whole-genome sequencing to assess its probiotic potential and analyze the correlation between its phenotype and genotype. In vitro experiments demonstrated that MG0718 possesses broad-spectrum antibacterial activity against pathogenic bacteria. In vitro experiments showed that MG0718 had broad-spectrum antibacterial activity against pathogenic bacteria such as Escherichia coli (E. coli), with an inhibition zone diameter of up to 13.67 ± 1.56 mm. It survived pH 2.5 for 6 h with only a 1.72 log10 reduction, and showed 0.78 and 1.11 log10 CFU/mL reductions in artificial gastric and intestinal fluids after 2 h. DPPH scavenging was 56.7% and total reducing power was 91.1%. In vivo, 7-day preventive administration maintained 100% survival against S. Typhimurium infection and alleviated weight loss. Bacterial loads in spleen, liver, and cecum dropped from 4.5, 4.5, and 4.2 to 3.6, 1.8, and 2.5 lg CFU/g, respectively. Whole-genome sequencing analysis indicated that the complete genome of MG0718 is 2,574,565 bp in length, containing 2813 CDS. Among these genomic components, 203 stress-related protein genes elucidate its superior environmental tolerance; one bacteriocin gene cluster, one EPS gene cluster and two secondary metabolite gene clusters provide the genetic basis for its antibacterial activity. Notably, no virulence factors were detected, ensuring the safety of the strain for application. In summary, the functional phenotypes of MG0718 are highly consistent with its genetic characteristics, identifying it as a probiotic candidate of significant developmental value. Future research should focus on clinical trials to further verify its practical benefits for human intestinal health and immunomodulation, thereby providing a robust scientific basis for its application in functional foods. Full article

This study aimed to analyze the effect of various phenolic acids introduced into pectin films on their ability to inhibit microorganisms. The antimicrobial activity of six phenolic acids—gallic, protocatechuic, caffeic, sinapic, coumaric, and ferulic acids—was verified against Bacillus subtilis bacteria. No inhibitory effect was observed when the acids were introduced into the substrates with the films, as the polysaccharide films served as a breeding ground for microorganisms. Bacterial growth was inhibited when pure acid was introduced to the substrate. Gallic and caffeic acid, at concentrations of 50 and 75 mM/dm³, respectively, completely inhibited bacterial growth. However, studies on pears have shown that such concentrations of phenolic acids are unsuitable for fruit coatings, as they lead to cloudiness and impaired visual appeal. Consequently, the lowest effective concentration was applied to fruit, reducing the total bacterial count from 2.59 ± 0.04 to 1.88 ± 0.22 log CFU/mL and mold and yeast counts from 2.11 ± 0.09 to 1.63 ± 0.10 log CFU/mL. The coating produced with the lowest tested concentration of gallic acid reduced the pears’ respiration rate. The amount of CO₂ released by coated fruit was approximately 4 mg/kg·h lower than that of uncoated pears, and the level of ethylene released was approximately 6 ppm lower. The addition of gallic acid at a concentration of 15 mM/dm³ to the coating reduced the growth of bacteria, yeasts, and molds. After 12 days of pear storage, the number of microorganisms in coated fruit was approximately 0.71 log CFU/mL lower for bacterial cells and 0.48 log CFU/mL lower for yeasts and molds. Full article

Respiratory disease remains a major challenge in pig production. This two-room pilot study evaluated whether room-level acoustic monitoring combined with physiological measurements could provide an early warning after an experimental Klebsiella pneumoniae challenge. Forty growing pigs balanced by sex and body weight were housed for 28 days in one control room and one challenged room (20 pigs/room; four pens/room). Challenged pigs were intranasally inoculated on days 8, 12, 16, and 20 with a culture whose dose was retrospectively verified by serial-dilution plating. Nasal and fecal samples were cultured on Klebsiella ChromoSelect agar, and colonies with expected morphology were enumerated as presumptive Klebsiella/K. pneumoniae colonies. A fine-tuned Audio Spectrogram Transformer (AST) classified five sound classes from facility-specific audio and was evaluated by group-blocked hold-out testing, five-fold group-blocked cross-validation, temporal deployment validation, and window-threshold sensitivity analysis. The model achieved hold-out macro-F1 of 0.947, five-fold macro-F1 of 0.928 ± 0.019, and 24 h deployment macro-F1 of 0.914. Presumptive nasal bacterial load was higher in challenged pigs at 1-week post-inoculation (log₁₀ 4.03 vs. 0.67). Group-size-standardized cough detections were also higher in the challenged room (54.84 vs. 36.80 detections/day), and daily coughing first exceeded the baseline threshold on day 8. Thresholds of 0.764 (control) and 1.115 (treatment) were obtained from an integrated score that included coughing, sneezing, ear temperatures, rectal temperature, and respiration rate; the treatment score and treatment–control contrast score first surpassed the threshold on day 8, and daily multimodal scores varied between groups (t = −6.636, p < 0.001). Integrated score improved discrimination of post-inoculation disturbance compared with cough detections alone (leave-one-day-out AUROC: 0.94 vs. 0.88). Because each condition was represented by one room, findings are exploratory temporal contrasts, not replicated treatment effects or a stand-alone diagnostic test. Full article

Background/Objectives: Multiplex and point-of-care (POC) diagnostics require each probe to detect one intended target while rejecting many closely related sequences under shared room-temperature conditions. The conventional focus on mismatch count is incomplete: two alignments with the same number of matches and mismatches can have very different off-target risks depending on whether mismatches are clustered or distributed. We introduce a simple visual heuristic that scores mismatch placement rather than mismatch count alone. Methods: Effective complementary island (ECI) score retained matched continuity after subtracting one base for each mismatch- or gap-exposed edge. The score is S_ECI = Σ_i ECI_i^2, and the design margin is ΔS_ECI = S_ECI (intended) − S_ECI (highest-scoring non-intended alignment by ECI). ECI is not a thermodynamic model; thermodynamics (ΔG37) is used separately to verify an adequate sensitivity floor. We retrospectively applied ECI to a fixed 39-target HPV capture-probe benchmark and to a public Affymetrix dataset contrasting clustered versus distributed mismatches at identical or near-identical mismatch counts. Results: In the HPV benchmark, ECI separated intended from off-target in 32/39 panels; ΔG37 favored the intended duplex in 31/39 panels; both layers were concordant in 36/39 panels. In the Affymetrix dataset (n = 8 probes, 2–4 mismatches), S_ECI correlated with reported log2 hybridization intensity (Pearson r = 0.92, p = 0.0014). Within the strict three-mismatch subset (n = 5), S_ECI remained correlated with intensity (r = 0.96; p = 0.010), while ΔG37 was uncorrelated (r = −0.04; p = 0.95), supporting the narrower claim that mismatch placement can affect signal even when mismatch count is fixed. Conclusions: ECI is not a replacement for thermodynamics, BLAST, target-accessibility analysis, empirical optimization, or machine-learning prediction. It adds one actionable readout: where to shift, shorten, or place a limited intentional mismatch so that intended retained continuity stays above the assay floor while the highest-scoring off-target island by ECI is fragmented. We provide a bench-ready workflow for multiplex, room-temperature, and POC probe design. Full article

The convergence of the Internet of Things (IoT), edge computing, and artificial intelligence (AI) is reshaping cyber defense in distributed cyber–physical environments. IoT-edge systems expose heterogeneous, resource-constrained, and intermittently connected devices to threats that unfold close to sensing and control processes, making purely signature-based or rule-based defenses increasingly insufficient. This article presents a structured review of AI for cybersecurity in IoT-edge systems from a systems-oriented perspective. Rather than surveying AI for IoT security in general, it organizes the literature around four practical lenses: AI methods, datasets and benchmarks, evaluation practice, and deployment constraints. The review reconstructs a workspace-verifiable corpus of 96 references, emphasizes literature published between January 2023 and April 2026 while retaining foundational benchmark papers, and uses a conservative 26-paper empirical subset for paper-level gap coding. Because this subset was purposively sampled and the original retrieval logs were not preserved, coded counts are interpreted as recoverable reporting signals and comparability indicators rather than field-level prevalence estimates. The revised synthesis further stratifies the coded evidence by task, model family, dataset, application scenario, metric type, and deployment signal, and translates deployment feasibility into a minimum reporting checklist and edge-hardware decision matrix. Within this evidence boundary, recent work remains dominated by intrusion and anomaly detection, with continued use of traditional machine learning, deep learning, federated learning, explainable AI, and graph-based approaches. However, experimentation remains concentrated around a small set of public benchmarks, while latency, memory, energy, communication overhead, operational robustness, and reproducibility are reported inconsistently. The field is therefore constrained less by classifier novelty than by benchmark concentration, weak deployment reporting, limited response-and-mitigation analysis, undercoverage of authentication, access-control, and trust-management tasks, and limited reproducible edge-aware evaluation. Full article

Modern vehicles rely on in-vehicle network protocols such as Controller Area Network (CAN) protocol, but these protocols were designed without encryption or authentication. Therefore, the vehicles are exposed to cyber attacks. Motion-based Intrusion Detection Systems (MIDSs) exploit correlation between physically related signals to detect attacks. However, we show that MIDSs are vulnerable, because correlation coefficient is invariant to positive linear scaling. Hence, an adversary may manipulate a signal while keeping its correlation high. In this paper, we propose a Correlation Scaling Attack (CSA) that forges wheel speed signals by scaling their original value while keeping the temporal trend consistent with the other signal. We analyze that correlation coefficient remains unchanged when the signal is forged. Consequently, the CSA evades conventional MIDSs. To mitigate this limitation of MIDS, we exploit covariance between two signals as a complementary indicator, since covariance provides magnitude information. We evaluate the proposed attack and defense mechanism using CAN log data collected from a real vehicle. Experimental results verify the effectiveness of CSA, and we demonstrate that CSA can be detected by observing covariance between two signals. Our research not only indicates that the CSA is a significant threat to cars, but provides a feasible mitigation exploiting the covariance. Full article

After a severe outage occurs, restoring a distribution network can take from several hours to days, making the secure and stable operation of restoration areas (RAs) critical. During a post-disaster partitioned operation, asymmetric controllable distributed generator (CDG) regulation capacity, non-controllable distributed generator (NDG) fluctuation risks, and concentrated high-value loads cause significant inter-area power imbalances. Soft open points bridge this resource gap by integrating electric vehicle charging directly into soft open points via vehicle-to-grid (V2G) technology; the resulting electric vehicle-embedded soft open points (EV-SOPs) acquire storage-like energy transfer capability. This paper proposes a boundary–node coordinated optimization strategy for post-disaster RA operation, which integrates CDGs, NDGs, smart switches, and EV-SOPs. Firstly, the boundary dynamic updating model with a multi-homogeneity indicator—load importance, NDG fluctuation risk, and CDG flexibility—enables adaptive resource allocation. Secondly, the optimal operational model of RA is formulated considering the various characteristics of facilities and topology constraints. Thirdly, EV-SOP uncertainties in response reliability, discharge power, and energy capacity are characterized by Bernoulli, log-normal, and truncated normal distributions, reformulated into a tractable mixed-integer quadratically constrained programming via chance-constraint interval linear transformation, and solved by a sequential weight-based priority search with hot-start strategy. Case studies on the IEEE 123-bus system verify the effectiveness of the proposed method. Specifically, the dynamic boundary strategy reduces the comprehensive weighted index by up to 29.10%; physical feasibility truncation reduces EV-driven load loss from 3.2073 MW to 3.1038 MW; and the sequential weight-based priority search with hot-start strategy achieves a cone constraint satisfaction measure of 9.3175 × 10⁻⁷, confirming robust convergence. Full article

Background/Objectives: Andrographolide (ADG) is a plant-derived compound with promising anticancer properties, but its medical use is limited due to poor water solubility and low bioavailability. This study proposes developing a gold-based nanocomposite drug delivery system, using a simplified synthesis method, to improve ADG’s hydrophilicity and enhance its delivery efficiency. Methods: A one-step method was used to synthesize gold nanocomposites with carbon quantum dots (CBQDs) and doped CBQDs acting as reducing and stabilizing agents. These nanocomposites were then conjugated with ADG and thoroughly characterized using multiple structural and spectroscopic techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), ultraviolet–visible spectroscopy (UV–Vis), transmission electron microscopy (TEM), Raman spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy. Hydrophilicity enhancement was evaluated using NMR-based log P measurements. Biological assessment involved cell viability assays and confocal microscopy studies in PC3 prostate cancer cells, along with the morphological evaluation of human red blood cells. Results: XRD confirmed the formation of crystalline, face-centered cubic gold nanoparticles, while spectroscopic analyses verified successful nanocomposite formation and ADG conjugation. NMR results showed enhanced hydrophilicity of ADG. Biological tests demonstrated that the nanocomposites were compatible with cells. Conclusions: This study presents a straightforward strategy for synthesizing gold-based nanocomposites that enhance the hydrophilicity and delivery potential of andrographolide, supporting their applicability as nanocarrier platforms for anticancer drug delivery. Full article

Cybersecurity has become a critical concern for individuals, organizations, and governments, especially with the rise of sophisticated cyberattacks and remote work environments. Traditional security approaches are no longer sufficient, leading to the adoption of advanced frameworks such as the zero-trust model, which operates on the principle “never trust, always verify.” This model enforces strict access controls and continuous monitoring across all network activities. Designing an intelligent zero-trust system is challenging due to the complexity of network environments and the evolving nature of malicious threats. This project proposes an advanced zero-trust architecture that integrates machine learning and multi-factor authentication (MFA) to strengthen security. Specifically, it employs Multilayer Perceptron models and k-Nearest Neighbors algorithms to analyze system logs and user behavior, enabling real-time anomaly detection and adaptive authentication mechanisms. The proposed framework is experimentally evaluated using the H-MOG behavioral–contextual authentication dataset, which captures multimodal user interaction patterns and supports continuous authentication analysis within Zero Trust environments. The integration of machine learning enhances the system’s ability to identify suspicious activities quickly and accurately, while MFA provides an additional layer of protection against unauthorized access. Moreover, the proposed framework emphasizes usability, ensuring that enhanced security does not impose excessive burden on users or IT teams. This allows the framework to respond more effectively to potential threats while maintaining usability. Overall, the proposed approach offers a practical and scalable solution that improves detection performance and strengthens continuous authentication and adaptive access control within Zero Trust environments. Full article

Acaryochloris marina is a distinctive cyanobacterium that uses chlorophyll d as its primary photosynthetic pigment and possesses two major light-harvesting systems: membrane-integral chlorophyll-binding Pcb/CBP complexes and water-soluble phycobiliproteins. How these antenna systems respond at the transcriptome level to contrasting light environments remains incompletely characterized. Here, we re-analyzed a publicly available RNA-seq dataset for A. marina MBIC11017 (NCBI BioProject PRJNA1130970), comparing cells grown under low-irradiance far-red light (LL-FR; 1.5–2 µmol photons m⁻² s⁻¹, 710-nm peak) and high-irradiance white light (HL-WL; 30–35 µmol photons m⁻² s⁻¹). Because light quality and irradiance both differ in this experimental design, the two effects cannot be separated; all transcriptional changes are therefore interpreted as responses to the combined LL-FR versus HL-WL contrast rather than to far-red wavelength alone. Of 8439 expressed genes, 1810 (21.4%) were significantly differentially expressed (adjusted p < 0.05). Using GFF-verified locus tags which corrected mis-annotations propagated in earlier analyses, the PS-I core gene set showed a mean log₂ fold-change of +1.96 (3.9-fold; 11/11 loci significant), whereas the PS-II core gene set showed a mean log₂ fold-change of +1.10 (2.1-fold; 12/20 loci significant). Light-harvesting genes showed the strongest response: 17/18 phycobiliprotein-pathway genes in KEGG amr00196 were upregulated, together with multiple putative Pcb/CBP loci (mean antenna log₂FC = +3.51; 11.4-fold). Weighted gene co-expression network analysis placed the antenna-associate genes examined here within a module positively correlated with the LL-FR condition (r = 0.802, p = 0.017), and STRING analysis supported an enriched network of predicted or known protein associations (1115 nodes, 4763 edges; PPI enrichment p < 1.0 × 10⁻¹⁶). Recent matched-irradiance experiments indicate that, at equal photon flux, far-red wavelengths reduce phycobilisome content relative to white light. The transcriptional pattern reported here is therefore most parsimoniously interpreted as predominantly a low-irradiance response, with possible wavelength-associated CA5 contributions that cannot be isolated in the present design. Overall, the analysis reveals coordinated transcript-level changes across plasmid-encoded reacquired phycobiliprotein genes, chromosomal Pcb/CBP loci, chlorophyll biosynthesis genes, and photosystem core genes, consistent with coordinated regulation of light-harvesting components in A. marina. Full article

Due to the complex issues of low porosity and low permeability in tight sandstone reservoirs, non-unified data measurement, and the limitation of traditional methods by empirical formulas and simple statistical models, which make it difficult to couple the correlation of parameters, how to quickly clean data, establish a comprehensive geological-engineering sweet spot evaluation method, and improve prediction accuracy and engineering decision-making effectiveness have become an urgent technical challenge. This study takes the logging and fracturing construction data in the L area as the data set, uses the Pearson correlation coefficient method to verify the nonlinear characteristics of features, and constructs a geological-engineering integrated intelligent decision-making algorithm based on the collaborative optimization of a dual-branch multi-layer perceptron and attention mechanism. The training results of the dual-branch multi-layer perceptron model and traditional machine learning methods are compared and analyzed. The results show that the prediction error of the adopted dual-branch multi-layer perceptron neural network model is 5.44%. The weight of geological factors in this area accounts for 51.71%, and the engineering factors account for 48.29%. This method has been field-applied in 25 wells in the L area, with a production coincidence rate reaching 94.66%. The sweet spots of tight sandstone reservoirs are mainly the H5 and H6 submembers. The deep integration of machine learning interpretability and geological engineering practice provides a new approach for sweet spot prediction. Full article

This article discusses the practical implementation of a prototype academic transcript storage system based on blockchain technology and smart contracts. The digital transformation of higher education requires reliable mechanisms for ensuring the integrity and verifiability of academic documents. It presents the design and experimental validation of a blockchain-based system for storing and verifying academic transcripts within the higher education system of the Republic of Kazakhstan. The proposed solution is based on an Ethereum Virtual Machine-compatible smart contract implemented in Solidity and deployed on a test network. The testnet was used as the experimental environment, and transaction monitoring was performed using the BlockScout v11.0.3 explorer. The architecture of the TranscriptStorage smart contract is presented, including a role-based access model, a data indexing mechanism using keccak-256, and storage of transcripts in a mapping structure (bytes32 => Transcript[ ]). The experimental results confirm the successful recording of the Transcript in the distributed ledger, event recording (Logs), and the correctness of the ABI encoding of input parameters (Raw Input), as well as a change in state (State Changes) reflecting the fee payment. The use of events is shown to enable cost-effective third-party data verification without the need to store the entire text in the contract state. The comparative results showed that the proposed system reduced gas consumption by 804.5% compared to Blockcerts, 48.8% compared to ECertChain, 82.5% compared to ShikkhaChain, and 43.5% compared to zkEVM. These improvements were achieved while maintaining high scalability, robust privacy features, and security, making it a practical solution for Kazakhstan’s educational system. Full article

Encrypted storage systems increasingly rely on governance mechanisms such as delegation, revocation, key updates, and policy evolution. While existing approaches provide strong guarantees for access enforcement, integrity, and transparency, they do not address a fundamental question: under which conditions can an observed sequence of governance events be accepted as a semantically valid evolution of authorization state? This work introduces a formal semantic framework for governance validity based on observable evidence. Governance is modeled as an admissibility-constrained state transition system in which events are accepted only if they satisfy explicit authorization, reference, temporal, revocation, and evidence conditions. The framework defines valid governance histories as sequences of admissible events; characterizes the conditions for deterministic state reconstruction; and establishes invariants capturing correctness properties such as revocation soundness, policy-constrained evolution, evidence completeness, non-equivocation, and temporal coherence. It also defines event-specific evidence obligations that support independent verification. The proposed approach is architecture-independent and does not prescribe specific enforcement or logging mechanisms, focusing instead on the semantic conditions required for accepting governance histories as valid from observable evidence. In addition, the framework can be instantiated as an independent verification layer that operates over observable governance traces without requiring access to internal system states. Full article