Search Results (3,443)

Security authorization relies on a target security profile that is relatively stable, whereas threats, exposure, control health, and assessment evidence change continuously. We present a two-stage decision-support model that separates operational remediation from structural insufficiency of an already authorized profile. The first stage tests whether an assurance-adjusted authorization-risk index can be restored to tolerance using admissible actions while the profile remains fixed. Only when this problem is infeasible does the second stage generate a minimum-burden candidate profile for assessment and reauthorization. The model defines an authorization envelope, preserves mandatory requirements, distinguishes candidate from authorized states, and treats any structural profile revision as a reauthorization event. In a 365-day synthetic critical-infrastructure benchmark with 100 paired Monte Carlo replicates, the proposed strategy averaged 0.27 days above the benchmark risk tolerance and 1.43 risk-driven profile revisions, compared with 0 days and 9.06 revisions for direct event-driven reoptimization. Matched ablation and sensitivity analyses support the remediation-first boundary while showing that its reduction in structural churn attenuates when attainable remediation quality is weaker; assumed control effectiveness remains the main source of model uncertainty. The results support frequent reassessment but selective, explainable profile revision under explicitly stated remediation and activation assumptions. This study is a computational proof of concept and does not replace legal authorization or empirical cyber-range validation. Full article

Advances in several physics domains open up novel paths to smaller scale, higher energy density opportunities to advance small systems for nuclear fusion. Here we survey both legacy and several novel “table-top” approaches which attract current interest. We furthermore address a few related practical and challenging nuclear science topics arising in the context of magnetic confinement and inertial confinement fusion. The contents emphasis includes: By example of solar fusion cycles we draw attention to aneutronic fusion reaction chains. Considering the natural isotopic abundances we assess more carefully the meaning of the term “limitless energy” in the context of actual fusion power realizations. We describe achievements in laser-driven proton–boron fusion, and extensions to a self-sustaining and nearly fully aneutronic proton–boron-nitride reaction cycle. We propose another aneutronic option, where the target is a mix of beryllium and light helium isotope; this 3-helium is arguably the most mentioned fusion component in this article. We look in depth at the plasmonic opto-electric field-enhancement for fusion, and at the particle (muon) catalyzed fusion option. We describe problems in harnessing the dt fusion for civilian use. We introduce space travel as forthcoming application of aneutronic fusion. Full article

Fission-gas-induced porosity significantly affects the irradiation creep behavior of U-10Mo alloy, which is critical for the safe and reliable design of fuel elements. In this study, a representative volume element (RVE) model with randomly distributed pores is developed to investigate the porosity-dependent irradiation creep of U-10Mo alloy. Based on homogenization theory and macroscale dimensional variation, the effective creep strain and creep rate are obtained, and the contributions of the U-10Mo skeleton and pores to the macroscale creep are quantitatively distinguished. Systematic parametric studies examine the effects of fission rate, tensile stress, and porosity on the effective irradiation creep response. The results show that (1) the effective creep strain scales linearly with both fission rate and applied stress and increases significantly with porosity; (2) the creep deformation of the U-10Mo skeleton dominates the macroscale creep, contributing from approximately 85% at 10% porosity to approximately 63% at 25% porosity, while the pore contribution increases with increasing porosity; (3) a porosity-dependent effective irradiation creep rate model is established, with only two fitting parameters, and it exhibits good internal consistency and extrapolability. Additional simulations at porosities of 5%, 18%, and 28% agree well with the fitting curve. The model is further validated by a U-10Mo/Al monolithic fuel element irradiation-induced deformation simulation, where the predicted thickness deformation shows reasonable agreement with experimental data. This work provides a practical and physically grounded model for multi-scale thermo-mechanical coupled analyses and engineering-scale fuel performance simulations of porous U-10Mo fuels. Full article

National energy systems are routinely linked to environmental performance through a single energy measure and a single outcome, usually carbon dioxide, leaving the breadth of that relationship untested. Using a balanced panel of 120 countries observed annually over 2011–2020 (1200 country-year observations) from the World Development Indicators, six energy variables are related—the renewable, fossil, and nuclear shares, energy intensity, electricity access, and electric power per capita—to five environmental outcomes: per-capita CO₂ and methane emissions, PM2.5 exposure, forest area, and freshwater withdrawal. Every outcome is estimated under the same ladder of estimators: pooled regression, two-way fixed effects with Driscoll–Kraay standard errors, instrumental variables, and Blundell–Bond system GMM. The energy mix explains carbon emissions strongly (R² = 0.73) and air pollution moderately (R² = 0.42) but accounts for little variation in freshwater withdrawal (R² = 0.06). Holding the renewable and nuclear shares and energy intensity constant, the fossil-fuel share carries no independent association with per-capita CO₂; this counter-intuitive null is reported openly and attributed to collinearity among competing shares of one supply mix, together with a measurement limitation in the underlying series, rather than to any emission-reducing property of fossil combustion. A higher renewable share is associated with lower per-capita CO₂ within countries and, cross-sectionally only, with greater forest cover; it shows no robust within-country association with air quality or freshwater withdrawal, and, in the dynamic specifications that pass the Hansen and AR(2) diagnostics, estimates attenuate because the outcomes are highly persistent. Cleaner energy is associated with lower carbon, not with uniform improvement across environmental dimensions. Full article

In the failure analysis of metallic materials, the observation of fracture-surface morphology by scanning electron microscopy (SEM) is an important means of determining the fracture mechanisms. In practice, however, interpretation of fracture-surface images relies heavily on expert experience and is readily affected by subjective factors when large batches of images must be examined. To address this issue, this paper proposes a classification model based on You Only Look Once version 8 (YOLOv8) and the convolutional block attention module (CBAM) for SEM images of metal fracture surfaces, aiming to identify four typical fracture-surface categories: cleavage, fatigue, dimple, and intergranular fracture. Considering that SEM images are mostly grayscale texture images, the model emphasizes the preservation of brightness, local texture, and edge-contour information during input processing and training augmentation, and introduces CBAM to optimize the channel and spatial responses of feature maps. Experimental results showed that at an input resolution of 1024, the YOLOv8-CBAM model achieved a Top-1 accuracy of 97.92% with only 1.53 M parameters. The proposed model achieved a favorable balance between observed classification performance and model complexity compared with the evaluated convolutional neural network (CNN) baselines. In addition, the gradient-weighted class activation mapping (Grad-CAM) results showed correspondence between the high-response regions of the model and certain fracture-surface morphology regions. Full article

Digital twins (DTs) are increasingly proposed for nuclear-reactor monitoring, diagnosis, prediction, control, and decision support, but the maturity of reported systems is difficult to compare because DT realization, physical anchoring, synchronization, adaptation, validation, real-time execution, and trustworthiness are often conflated. This systematic scoping review maps substantive DT research for nuclear fission reactors and nuclear power plants using a strict substantive-DT definition and a multidimensional evidence-coding framework covering DT realization, physical anchoring, adaptation, validation, real-time capability, and trustworthiness. Scopus, Web of Science Core Collection, and IEEE Xplore yielded 845 database records and 590 unique records after deduplication. A multi-pass title/abstract workflow classified 182 records as Include, 397 as excluded from the primary-study stream, and 11 as Uncertain, yielding 193 reports sought for retrieval. Despite iterative multi-source retrieval attempts, 96 reports could not be retrieved. One additional Uncertain conference report was linked to an already retained journal report and was not treated as an independent report; 96 retrieved reports underwent primary full-text eligibility assessment. Seventy-one retrieved and eligible peer-reviewed primary reports met the inclusion criteria. Of these, 44/71 (62%) were Tier A implemented or integrated DT systems. Physical anchoring ranged from P0 simulation-only realization (39/71, 55%) to P3 operating-reactor or plant evidence (9/71, 13%); within P3, four reports used retrospective/offline operating data and five demonstrated live or near-live reactor/plant linkage. Validation evidence comprised 40/71 reports (56%) with development-linked validation (V1), 26/71 (37%) with separated empirical or benchmark validation (V2), and 5/71 (7%) with prospective, challenge-based, or independently confirmed validation (V3). Adaptation was limited: 51/71 reports (72%) demonstrated no adaptation (A0), 3/71 (4%) episodic or intermittent recalibration (A1), 5/71 (7%) online state synchronization/estimation (A2-S), 12/71 (17%) online parameter/model adaptation (A2-P), and none met the A3 criterion. Computational real-time capability was supported in 42/71 reports (59%), whereas integrated real- or near-real-time execution was demonstrated in 27/71 (38%); live physical-data ingestion, explicit recurring physical-to-digital synchronization, and automatic DT-to-physical actuation were demonstrated in 11/71 (15%), 5/71 (7%), and 1/71 (1%), respectively. Hybrid modelling was the dominant paradigm (34/71, 48%). Trustworthiness was asymmetric: calibration and robustness/OOD assessment were much more common than uncertainty quantification, explainability, cybersecurity, traceability, human oversight, or demonstrated regulatory readiness. A targeted data audit identified only four reports linked to clearly open and reusable benchmark or downloadable resources. Family-aware, quality, physical-evidence, temporal, and data-access sensitivity analyses did not materially change the central conclusions. Nuclear-DT maturity is best understood as multidimensional; future progress requires staged physical validation, bounded adaptive synchronization, trustworthy decision support, reproducible data/model provenance, and independent deployment-oriented validation. Full article

We examine point predictions and empirical uncertainty in a reproducible benchmark of 1211 simulated Nuclear Power Plant Accident Data (NPPAD) trajectories from 12 accident classes, including 100 hot-leg loss-of-coolant accident (LOCA) trajectories. Onset is obtained retrospectively from transient reports; the endpoint is post-onset diagnosis, not online event detection. Seven deterministic statistics of 96 shared sensors provide 672 features at 10-, 20-, 30-, 60-, and 96-sample windows. Repeated grouped holdout uses five fixed, stratified partitions, keeping equal complete numerical records in the same training, calibration, or test role. The 1109 equality blocks do not establish independent simulation scenarios; all results are exploratory within-cohort estimates. At 30 samples, histogram gradient boosting and random forest attained mean accuracies of 0.9952 and 0.9927, whereas logistic regression and a multilayer perceptron attained 0.8663 and 0.7908. Random forest LOCA regression yielded a mean absolute error of 0.8206 percentage points and empirical 90% interval coverage of 0.9400 at this window. Exact calibration counts, conformal ranks, thresholds, covered case counts, and binomial reference intervals expose substantial small-sample uncertainty. Five calibration cases per LOCA class force every 90% and 95% Mondrian threshold to infinity. Removing a dimensionally inappropriate ratio and changing numerical scaling reduced historical neural network extremes but did not eliminate physically unreasonable predictions. Sensor corruption and ordered-severity extrapolation degraded clean-calibrated uncertainty. The benchmark supports transparent comparison within this simulator cohort; it does not establish independent-scenario generalization, conformal exchangeability, or plant readiness. Full article

Automated welding processes have become increasingly critical in the shipbuilding, automotive, and aerospace industries. Precise weld seam tracking is essential for reliable automated welding, as torch misalignment causes weld defects and significant variations in bead geometry. In this study, fillet welding experiments using flux-cored arc welding (FCAW) were performed under 15 conditions comprising five torch offset levels (−2, −1, 0, +1, +2 mm; positive values denote displacement toward the vertical plate) and three welding speeds (6.0, 7.5, and 9.0 mm/s). Twelve characteristic points were extracted from three bead cross-section zones—the vertical penetrated zone, horizontal penetrated zone, and bead surface zone—and predicted using a nonlinear exponential regression model. All regression coefficients were statistically significant at the 95% confidence level. Piecewise cubic Hermite interpolation (PCHIP) was subsequently applied to reconstruct smooth two-dimensional bead profiles from the predicted feature points. Quantitative validation of the primary leg-length parameters (L_V and L_H) yielded low prediction error (RMSE below 0.7 mm and MAE below 0.5 mm) across all tested conditions. The proposed algorithm runs in under 1 ms and is therefore well-suited for real-time integration into seam-tracking control systems. Full article

Fretting wear at metal O-ring sealing interfaces involves the coupled processes of debris generation, entrapment, and oxidation. Existing accelerated life testing relies on similarity theory, yet it overlooks the dominant role of third-body behavior in governing wear regime transitions, so equivalence between test and service conditions lacks a physical basis. This study investigates reciprocating fretting wear of an Inconel 718/304 stainless steel pair and reinterprets wear evolution using third-body dynamics. Laser scanning confocal microscopy, energy-dispersive spectroscopy, and real-time friction monitoring reveal three distinct stages: debris generation with mild damage, oxide layer formation with steady wear, and oxide layer fracture with material spalling. The friction coefficient passes through running-in, steady-state, and sharp-rise phases, while oxygen content on the wear track rises from 2.1 wt.% to 18.3 wt.%. One-way ANOVA shows significant differences among all stages. Based on Berthier’s theory, a state-evolution model is developed that treats third-body oxidation degree and cumulative friction energy dissipation as equivalence criteria. Friction energy and wear mass correlate linearly (R² = 0.94), giving an effective wear coefficient of 6.3 × 10⁻⁷ mg/J and acceleration exponents of m₁ = 9.77 for pressure and m₂ = −8.71 for frequency. Independent validation shows that the model compresses test duration by 51%, with relative errors below 18% for wear mass, fractal dimension, and fractal roughness. By shifting the focus of accelerated testing from dimensional analysis to mechanism-preserving state tracking, this work provides a failure analysis framework that identifies the root cause of seal degradation as third-body oxidative spalling and offers practical preventive actions for nuclear metal seal reliability assessment. Full article

Sargassum horneri is a brown alga with diverse biological activities; however, the influence of geographical origin on its functional properties remains poorly understood. This study comparatively evaluated the antioxidant and anti-inflammatory activities of S. horneri extracts originating from the East, South, and West Seas of Korea. Total phenolic content (TPC) and total flavonoid content (TFC) were determined, and antioxidant activity was assessed using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical-scavenging assays and the ferric reducing antioxidant power (FRAP) assay. Anti-inflammatory activity was evaluated in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. The South Sea extract exhibited the highest TPC and showed strong overall antioxidant activity. LPS stimulation reduced cell viability and markedly increased nitric oxide (NO) production, whereas treatment with the regional extracts improved cell viability and suppressed NO production to varying degrees. Among the three extracts, the South Sea extract exhibited the most pronounced inhibitory effect on LPS-induced NO production. Based on its overall bioactivity profile, the South Sea extract was selected for further mechanistic investigation. The extract reduced cyclooxygenase-2 (COX-2) expression and nuclear factor-kappa B (NF-κB) phosphorylation. Furthermore, extracellular signal-regulated kinase (ERK) phosphorylation was markedly reduced, particularly at 400 μg/mL, while c-Jun N-terminal kinase (JNK) phosphorylation showed an overall decreasing tendency at higher concentrations without a strictly concentration-dependent response. In contrast, p38 phosphorylation was not suppressed. These findings suggest that the anti-inflammatory activity of the South Sea S. horneri extract may be associated, at least in part, with modulation of ERK/JNK and NF-κB signaling. Overall, this study demonstrates regional variation in the antioxidant and anti-inflammatory properties of S. horneri and identifies the South Sea-derived extract as a promising marine resource for the development of functional materials. Full article

Background/Objectives: Understanding genomic responses to environmental contamination is essential for assessing long-term ecological and genetic risk. The free-breeding dogs inhabiting the Chornobyl Exclusion Zone (CEZ) offer a unique, multigenerational model for investigating these responses within a chronically contaminated landscape. Following the 1986 radioecological disaster at the Chornobyl Nuclear Power Plant, and subsequent remediation activities, the surrounding environment was contaminated by ionizing radiation, heavy metals, organic compounds, and other environmental toxicants. Our previous work identified significant genetic differentiation between two distinct but geographically proximate dog populations within the CEZ, a pattern not explained by breed composition, spatial isolation, or increased mutation rates. Methods: In this study, we tested the hypothesis that the dogs residing at the nuclear power plant show genomic signatures consistent with adaptive evolution in response to chronic environmental stressors. This work extends prior population-genetic analyses by integrating whole-genome selection scans with estimates of effective population size and comparative measures of population divergence, providing a framework to evaluate whether the observed genomic differentiation is more consistent with selection than demography. Using these whole genomic data, we evaluated chromosome-level F_ST metrics, estimated effective population size (N_e), and analyzed a geographically isolated island dog population as a comparative reference for genetic divergence. Results: We continued to find that the levels of genetic differentiation are not comparable to other free-breeding dog populations. Genome-wide outlier analyses identified candidate loci showing evidence of positive selection across multiple metrics. Conclusions: Genes associated with these loci were enriched for biological functions related to DNA replication and repair, cell death and survival, cellular compromise, immune response, and cancer-associated pathways, suggesting biologically plausible mechanisms through which chronic environmental stress could influence fitness. These findings are consistent with directional selection in the nuclear power plant population, which could potentially be associated with the environmental variables present, and advance our understanding of local adaptation and genomic resilience under chronic environmental contamination. Full article

Nuclear new-build megaprojects frequently experience schedule overruns that prolong the pre-operational phase and leave installed systems exposed prior to commissioning and commercial operation. While existing research has extensively examined the causes of nuclear project delays and their impacts on cost and schedule performance, comparatively limited attention has been devoted to lifecycle risks associated with idle but installed systems. During extended delays, systems require continuous preservation, monitoring, and re-validation, increasing the risk of technological obsolescence, documentation deterioration, and reactivation complexity. This study investigates installed-asset exposure in delayed nuclear construction projects and develops a conceptual governance framework for managing this condition. It adopts a conceptual research approach supported by research question-driven thematic literature synthesis of academic, institutional, regulatory, and technical evidence. Based on this analysis, the study develops a mechanism explaining how extended delays generate secondary lifecycle risks and cost amplification through prolonged exposure of installed systems. Building on this mechanism, the paper introduces the Pre-Operational Asset Governance Framework (Pre-OAGF) as a delay-triggered coordination layer. The framework comprises the Governance Structure, Activation Logic, and Asset Monitoring Framework, which are designed to coordinate preservation across contractual interfaces and support commissioning readiness. Full article

Background/Objectives: Myocardial perfusion scintigraphy (MPS) is a cornerstone non-invasive imaging modality for assessing myocardial ischemia and infarction. This pilot study aimed to evaluate the feasibility of using ChatGPT-4 for automated preliminary clinical reporting in MPS and to identify specific scenarios in which large language model (LLM) performance is robust versus inadequate. Methods: A comparative analysis was conducted using 30 consecutive de-identified MPS cases spanning a broad spectrum of clinical scenarios. Structured clinical data were input into ChatGPT-4 to generate AI-based preliminary reports, which were then compared with reports prepared by two experienced nuclear medicine physicians. Reports were independently evaluated using four criteria—clinical accuracy, report structure, terminological appropriateness, and overall comprehensibility—scored on a 5-point Likert scale. Inter-observer agreement was assessed using Cohen’s kappa. Results: ChatGPT-4 demonstrated strong performance in report structure (median 5), terminological appropriateness (median 4), and overall comprehensibility (median 5), consistently producing well-organized and coherent reports. However, clinical accuracy was significantly lower compared with physician reports (median 4 vs. 5; p = 0.002; effect size r = 0.52), particularly in complex cases such as multivessel ischemia and mixed pathologies, where outputs were occasionally superficial or lacked specificity. Inter-observer agreement between evaluating physicians was substantial (Cohen’s κ = 0.78; 95% CI: 0.64–0.92). Conclusions: ChatGPT-4 shows promise as a supportive tool for preliminary MPS reporting and medical education, but its limitations in higher-order clinical reasoning necessitate careful human oversight. These findings are preliminary and require validation in adequately powered, multicenter studies before any clinical implementation. Full article

Aqueous Homogeneous Reactors (AHRs) exhibit a strong coupling between radiation chemistry and reactor kinetics, wherein radiolysis-induced gas production critically governs transient behaviors via void reactivity feedback. Historically, the predictive accuracy of reactor dynamic models has been severely constrained by the lack of reliable radiolytic yields (G-values) under extreme high-linear-energy-transfer (LET) conditions. To bridge this gap, this study establishes a physically consistent multiscale framework by integrating recently published high-LET G-values—derived from heavy-ion radiolysis experiments—into a macroscopic reactor dynamics model. Utilizing these experimentally validated microscopic source terms, which accurately capture pronounced track-structure effects, a stiff radiation-chemical kinetic model was developed. This model incorporates water radiolysis, nitrate effects, and secondary redox reactions to enable the precise prediction of steady-state gas generation. The calculated hydrogen production rates demonstrate excellent agreement with experimental benchmark data, yielding a relative deviation within ±13%. Furthermore, the chemically derived gas source terms were dynamically coupled into a comprehensive multiphysics framework encompassing point kinetics, conjugate heat transfer, and bubble transport. Validation against the TRACY and SILENE benchmark experiments confirms that the proposed model successfully reproduces transient power excursions, oscillatory behaviors, and quenching phenomena driven by void feedback. This work establishes a robust theoretical and computational basis for the safety analysis of solution-type nuclear reactors. Full article

Rolling bearing faults typically exhibit sideband structures in the frequency domain, yet conventional blind deconvolution methods operate in the time domain and rely on prior fault periods. To reduce this dependence and broaden applicability, this paper presents a frequency-domain blind deconvolution method based on weight feature extraction (WFE-FDBD). The method constructs rectangular pulse weights with a finite bandwidth to mitigate the effects of limited frequency resolution and minor sideband fluctuations. By incorporating the correlated kurtosis index, an inverse filter with adaptive period adjustment is built, enabling automatic selection of the fault period. Operating directly in the frequency domain, the proposed approach effectively enhances fault-related harmonics and suppresses noise interference. Both simulated and experimental signals validate the WFE-FDBD method, demonstrating its capability to diagnose localized defects on inner and outer raceways of rolling bearings. Full article