Search Results (3,026)

Accurate modeling of post-peak rock behavior is fundamental to the safe design and stability assessment of underground excavations in high-stress environments. In this context, the dilation angle is a key constitutive parameter, typically expressed as a function of plastic shear strain and confinement. The model proposed by Zhao and Cai reproduces the complex evolution of the dilation angle with notable accuracy; however, its reliance on nine interdependent fitting coefficients hinders practical calibration and numerical implementation. This study introduces a simplified model, derived from the Zhao and Cai model and guided by the conceptual framework of Zhao and Li, that reduces the number of fitting parameters to four while preserving the nonlinear dependence of the dilation angle on plastic shear strain and confinement. When calibrated against triaxial compression data, the proposed expression attained a coefficient of determination of 41.14%, markedly exceeding the 26.73% obtained with the original formulation. Although numerical simulations in FLAC^2D v. 9.7 did not fully capture the experimental response, the proposed model outperformed the Zhao and Cai model, which systematically underestimated the evolution of the volumetric strain. The results demonstrate that the proposed model provides a more tractable description of dilatancy and an improved numerical approximation of the volumetric response. Full article

Shear modulus degradation curves are fundamental inputs in nonlinear site response analyses and are conventionally normalized by the small-strain shear modulus, G_max, defined at shear strains on the order of γ ≈ 10⁻⁴%. In shaking table experiments, however, reliable measurements at very small strains are often unattainable due to instrumentation resolution and strain demand limitations. Consequently, normalization is frequently performed using the shear modulus at a higher reference strain (γ = 0.01%). The impact of this alternative normalization on the resulting shear modulus degradation relationship has not been systematically evaluated. This study investigates the influence of normalization strain level on shear modulus degradation behavior using stress–strain relationships reconstructed from shaking table acceleration records. The shear modulus values were computed from individual hysteresis loops. The shear modulus normalized by G_max estimated from empirical correlations was compared with the shear modulus normalized by its value at γ = 0.01% directly obtained from shaking table measurements. Results indicate that normalization at γ = 0.01% produces slightly lower normalized modulus values for shear strains exceeding 0.01% compared with the curve normalized by G_max. Normalization using G_γ=0.01% resulted in reduced scatter and uncertainty. Full article

Forest fire susceptibility mapping is an important component of disaster risk reduction, particularly in transitional climatic zones such as Anhui Province, China. Traditional approaches often rely on expert weighting (AHP) or linear assumptions, which may be insufficient for capturing the complex, non-linear interactions of fire drivers. This study develops a data-driven framework integrating 816 field-surveyed fuel plots with MODIS active fire data (2000–2025). We applied a systematic preprocessing pipeline, including 1–99% Winsorization to reduce the influence of sensor outliers, Non-Linear Gamma Curvature Normalization to represent asymmetrical risk responses, and a spatial buffer-based pseudo-absence protocol combined with semantic land-cover masking to reduce label ambiguity and macro-environmental bias. Benchmarking against seven machine learning algorithms on a naturally balanced dataset showed that the Random Forest (RF) model achieved the highest test-set performance among the evaluated models (Test AUC = 0.831). Youden’s J statistic was used to define a data-driven risk threshold. The results suggest that topographic configuration and forest stand density act as important baseline constraints and interact with physiological moisture stress indicators to influence fire susceptibility. The species-level risk analysis was broadly consistent with ecological expectations: coniferous forests showed the highest predicted high-risk proportion (79.10%), whereas soft broadleaves showed a substantially lower predicted high-risk proportion (4.29%). Spatial mapping indicated a “South-High, North-Low” pattern associated with topographic forcing and fuel continuity, which may provide useful information for regional fire management and the planning of green firebreaks. Full article

Accurate prediction of welding-induced deformation is essential for dimensional control in large-scale ship block construction. In production design, transverse shrinkage directly governs the cutting allowance and shrinkage margin among various deformation modes. The inherent strain framework is widely used due to its computational efficiency, but the interaction between the implementation of equivalent loads and geometric nonlinearity has not been systematically investigated. This study evaluates two conventional loading representations: the equivalent nodal force method and the equivalent thermal load method, under both linear and geometrically nonlinear analysis formulations. In linear elastic analysis, both representations are equivalent and successfully provide identical, stable in-plane shrinkage predictions because both methods utilize input loads formulated from the same target inherent deformation. However, in shipbuilding practice, a geometrically nonlinear formulation is frequently required to capture large-displacement behaviors or structural instabilities in thin-walled assemblies. When geometric nonlinearity is introduced into these shrinkage predictions, a critical discrepancy emerges depending on the load implementation: the equivalent nodal force method violates the physical basis of shrinkage prediction by introducing unwanted out-of-plane deformation artifacts. This is a numerical artifact arising from the interaction of localized artificial compressive stresses with the stress-dependent geometric stiffness matrix. In contrast, the equivalent thermal load method is robust and always preserves the target in-plane shrinkage without any undesired out-of-plane geometry. Therefore, even though both methods are robust in the linear regime, the equivalent thermal load method is recommended when a geometrically nonlinear formulation is involved to ensure numerical consistency and reliability in production design. Full article

Hair is an easily obtainable, non-invasive biomatrix that allows for the assessment of long-term physiological responses to environmental and anthropogenic stressors in wildlife populations. Herein, an ultra-high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UHPLC-ESI-MS/MS) method was validated to verify its suitability for the simultaneous determination of cortisol (CORT) and its metabolite, cortisone (CORN), in hair samples collected from wild-living grey wolves (Canis lupus). Hair samples were extracted with methanol and purified using solid-phase extraction on Strata-X cartridges, which enabled effective mitigation of matrix effects. Data for the glucocorticoids were normalized using internal standards. The method demonstrated good linearity for the target stress hormones, with satisfactory precision (RSD < 15%) and limits of quantification of 4.13 pg/mg for CORT and 2.49 pg/mg for CORN in the hair matrix. Analysis of authentic wolf hair samples revealed CORT and CORN concentrations in the ranges of <4.13–11.86 pg/mg and <2.49–3.67 pg/mg, respectively. The CORT results showed a strong positive correlation with those obtained using enzymatic immunoassays. The method may be applied to assess the impact of stressors on the welfare of wolves, e.g., providing a useful tool for monitoring recovering European populations as they face new challenges associated with expansion into potentially suboptimal habitats. Full article

While psychological resilience (PR) is critical for adaptive stress responses, its neurophysiological substrates remain unclear. Given that gamma activity (30–130 Hz) is implicated in the sensory integration and cognitive control necessary for emotion regulation, this study utilized multimodal EEG and psychometric assessments (N = 100) to evaluate resting-state eyes-open (EO) and eyes-closed (EC) oscillatory dynamics in three experimental stages. Resilience was quantified using the Connor–Davidson Resilience Scale (CDRISC-25). Following a hierarchical signal-discovery protocol, a whole-head survey was conducted to identify sites of interest, providing independent data for hypothesis formation. Following rigorous artifact control (EOG-verified ICA; 90% mean epoch retention) and statistical adjustment for multiple comparisons (Benjamini–Hochberg FDR), a robust spectral cluster was identified at the left frontopolar site (FP1), showing consistent inverse correlations with resilience across 30–50 Hz, 50–70 Hz, and 70–90 Hz sub-bands (all p FDR = 0.043). Crucially, findings were strictly lateralized, with the adjacent FP2 site remaining non-significant. Results highlight a lateralized neurophysiological signature of resilience at the left frontal pole, arguing against diffuse myogenic contamination. Next, source localization (eLORETA) and functional connectivity (Lagged Linear Coherence) were utilized to characterize spatial dynamics. Source analysis revealed noteworthy activity near the left anterior cingulate and medial prefrontal cortex. Furthermore, functional connectivity analysis showed significant coherence correlates across nodes linked with the default mode network (DMN). Notably, the 70–90 Hz sub-band emerged as a consistent correlate across all three experimental stages. Prioritizing statistical parsimony over complex mediation, these preliminary, hypothesis-generating findings suggest that site-dependent and frequency-specific gamma activity may provide neurophysiological insight toward adaptive flexibility. In particular, the 30–90 Hz spectral cluster at the left frontopolar region appears to be associated with the resting-state profile of resilient individuals in complex, as-yet-unspecified ways. These pilot data suggest further investigation is warranted, potentially providing a foundational target for future longitudinal research into the oscillatory signatures of adaptive flexibility. Full article

To reduce carbon emissions from cement-treated aggregate base layers and examine the nonlinear service behavior of semi-rigid materials, supersulfated cement (SSC) was used to replace ordinary Portland cement (OPC). A dynamic triaxial loading protocol was adopted to separate the effects of bulk stress and shear stress on the dynamic resilient modulus of supersulfated cement-treated aggregate (SSC-CTA). A fatigue damage equation was developed based on the strain energy balance during cracking, and Paris’ law damage parameters were introduced to compare the damage growth rates of SSC-CTA and ordinary Portland cement-treated aggregate (OPC-CTA). Finite element analysis and partial differential equations were further used to link the stress-dependent resilient modulus with structural fatigue life. The results show that SSC-CTA had a lower dynamic resilient modulus than OPC-CTA under the same stress state. The average resilient modulus of SSC-CTA was 978 MPa, which was 15.47% lower than that of OPC-CTA. For both materials, the modulus increased with bulk stress and decreased with octahedral shear stress, and the NCHRP 28A model accurately predicted this nonlinear behavior. Although SSC-CTA had a lower modulus, its indirect tensile strength reached 864.3 kPa, representing a 52.65% increase compared with OPC-CTA. The Paris’ law parameters further indicated that SSC reduced the damage growth rate during crack propagation. The finite element results showed that the predicted structural fatigue life of SSC-CTA increased by 4.49–35.90% under different load levels. Full article

Background: Depressive symptoms (DepS) are prevalent among breast cancer patients and are associated with poor treatment outcomes and prognosis. Oxidative stress has been implicated in the pathophysiology of depression, and dietary antioxidants may be associated with oxidative balance. This study aimed to evaluate the association between the composite dietary antioxidant index (CDAI) and DepS, and to explore the statistical relationships between CDAI, oxidative stress biomarkers, and DepS. Methods: In this cross-sectional study, 302 breast cancer patients were enrolled, of whom 113 provided blood samples for the assessment of plasma oxidative stress biomarkers. Dietary intake was assessed using 3-day 24 h dietary recalls. The CDAI was calculated based on the intake of six dietary antioxidants, including vitamins A, C, and E, zinc, selenium, and manganese. DepS were defined as a score of ≥8 on the Hospital Anxiety and Depression Scale—Depression subscale (HADS–D). Results: A total of 102 patients (33.77%) exhibited DepS, and these patients had significantly lower CDAI scores and reduced plasma levels of superoxide dismutase (SOD) and glutathione (GSH), compared with those without DepS (all p < 0.05). CDAI and dietary zinc intake were non-linearly associated with DepS (p for non-linearity <0.05). Vitamin C intake was inversely associated with DepS (OR = 0.989; 95% CI: 0.984, 0.993; p < 0.001). CDAI scores were positively associated with SOD and GSH levels (both p < 0.001), while SOD and GSH were inversely associated with DepS (SOD: β = −0.610; GSH: β = −0.900; both p < 0.001). In exploratory mediation analysis, GSH and SOD statistically accounted for part of the association between CDAI and DepS (ACME = −0.192 and −0.228, respectively; all p < 0.01). Conclusions: Higher dietary antioxidant intake, as reflected by CDAI, is associated with lower DepS in breast cancer patients, and oxidative stress biomarkers may be statistically involved in this association. Full article

Soil liquefaction is a major seismic hazard that threatens coastal infrastructure. Yet, accurate prediction of the liquefaction factor of safety (FS) remains challenging because of the complex nonlinear interactions among geotechnical and stress-state variables. This study proposes a novel leakage-aware Hybrid Stacking Ensemble (HSE) framework for the direct prediction and geotechnical interpretation of FS in the Port Sudan coastal plain. The proposed model combines Random Forest, Extra Trees, and Gradient Boosting Regressors through a RidgeCV meta-learner while restricting the predictor set to independent spatial, geotechnical, and stress-state variables to prevent data leakage. A database comprising 534 Standard Penetration Test (SPT)-based observations from 61 boreholes was used to evaluate the proposed model against K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Random Forest, and XGBoost. The Hybrid Stacking Ensemble achieved the highest predictive performance, with a test RMSE of 0.058, an MAE of 0.032, and an R² of 0.985, demonstrating improved accuracy, robustness, and generalization over the benchmark models. SHAP-based interpretation identified N1(60)cs as the dominant predictor of FS, followed by relative density, depth, and stress-related variables, confirming that the proposed framework learned physically meaningful relationships consistent with established liquefaction mechanics. The contribution of this work lies in the development of a Hybrid Stacking Ensemble framework for the direct prediction of the liquefaction factor of safety (FS), integrated with a leakage-free modeling workflow and explainable machine learning techniques, rather than in the development of a new machine learning algorithm. Full article

To accurately characterize the nonlinear hyper-viscoelastic mechanical behavior of rubber materials under large deformation and stress relaxation conditions, this study investigates fluororubber (FKM) and hydrogenated nitrile rubber (HNBR) with different hardness levels through uniaxial mechanical tests and stress relaxation experiments. A constitutive parameter identification method based on hyperelastic models and the parallel rheological framework (PRF) model is established. First, several representative hyperelastic models, including the Neo-Hookean, Mooney–Rivlin, Yeoh, Ogden, Arruda–Boyce, and Van der Waals models, are comparatively evaluated. The results show that the Ogden model with (N = 3) provides the highest fitting accuracy for the large-deformation responses of FKM and HNBR with different hardness levels, with coefficients of determination (R²) ranging from 0.9879 to 0.9948. Subsequently, the Prony series parameters are identified from the stress relaxation data and converted into the initial parameters of the linear PRF model. To overcome the limitations of the linear PRF model in predicting nonlinear relaxation behavior, the PRF parameters are further optimized using the Isight data matching method combined with the Hooke–Jeeves algorithm. Finite element validation demonstrates that the optimized nonlinear PRF model can accurately predict the stress relaxation behavior of both FKM and HNBR. The mean absolute percentage errors of FKM60, FKM70, and FKM80 are 2.67%, 1.57%, and 2.56%, respectively, while those of HNBR60, HNBR70, and HNBR80 are 2.16%, 2.72%, and 2.58%, respectively. These results indicate that the combination of the Ogden (N = 3) hyperelastic model and the optimized nonlinear PRF model can effectively describe the large-deformation and time-dependent viscoelastic responses of rubber materials, providing a reliable constitutive modeling basis for finite element analysis and parameter calibration of rubber sealing structures. Full article

Purpose: The assessment of endocrine status in elite athletes is typically linked to training load and perceived stress; however, the relationship between hormonal parameters and psychosomatic and stress indicators remains insufficiently understood. This study aimed to investigate the associations between endocrine status and stress, mood, and psychosomatic status indicators in elite handball players. Methods: In a cross-sectional study, salivary cortisol (with no strict control over wake-up time), testosterone, and—in female athletes—17-β-estradiol concentrations were assessed in 584 elite handball players aged 14–35 years using ELISA. Psychological variables were evaluated using the Perceived Stress Scale (PSS), Profile of Mood States (POMS), and the Health Behavior in School-aged Children Symptom Checklist (HBSC-SCL). Associations were examined using non-parametric tests and general linear models adjusted for age. Results: Hormonal and psychological variables demonstrated significant age-related trends. No significant associations were observed between hormonal parameters and perceived stress or mood disturbance (values for the general linear model (GLM) were all p > 0.05). In contrast, psychosomatic symptom severity was significantly associated with cortisol levels in male athletes (GLM, p < 0.001) and testosterone levels in female athletes (GLM, p = 0.009). Multivariate analyses confirmed the relevance of psychosomatic symptoms and indicated interaction effects between stress-related factors. Conclusion: Psychosomatic symptoms were more closely associated with endocrine status than with perceived stress or mood disturbance in elite handball players. However, these associations were characterized by relatively small effect sizes, indicating that psychosomatic symptoms explain only a limited proportion of the variance in hormonal parameters. These findings suggest that psychosomatic indicators may provide a more sensitive reflection of physiological strain and support the use of integrated monitoring approaches combining endocrine and psychosomatic measures in elite sport. In practical terms, routine monitoring of psychosomatic symptoms alongside hormonal measures may help practitioners to identify early signs of physiological strain and support timely adjustments in training load and recovery strategies. 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

Masonry tunnels built between late 19th and early 20th century constitute a widespread asset of the existing infrastructure network and currently require systematic condition assessment, monitoring, and maintenance interventions. Despite some existing regulatory frameworks, performing detailed assessments on tunnels with masonry linings remains a difficult task due to the significant uncertainties and the complex behavior of masonry. To address this gap, this work proposes a Simplified Approach (SA) for the structural assessment of masonry tunnels. A formulation adapted from classic static methods is proposed for the rapid assessment of the structural capacity of masonry linings. The proposed approach was applied and evaluated through a well-documented case study, in which the actual stress states were obtained with on-site measurements, that were employed to calibrate the model parameters by means of best fitting. The SA was employed to conduct a detailed stress verification along the entire lining, demonstrating its effectiveness as a calibrated tool for the large-scale safety assessment of historical tunnels, for the identification of critical sections that may require subsequent non-linear Finite Element Method analysis for Ultimate Limit State verification. Full article

The development of potash deposits under complex mining and geological conditions requires the implementation of efficient geotechnologies, including backfilling of mined-out voids. Preserving the water-protective strata and preventing mining-induced accidents are impossible without accurate prediction of the stress–strain state of the backfill mass. Traditional models, based on the Mohr–Coulomb criterion, are unable to properly describe physical and mechanical processes occurring in crushed salt rock, including the transition from dilatancy to compaction and nonlinear hardening. This requires the application of specialized models such as the SRP model. The aim of this study is to investigate the mechanical response of crushed salt rock backfill material under complex loading conditions and to calibrate the parameters of the SRP model in order to improve the accuracy of geomechanical calculations. The shape of the plastic flow surface in the deviatoric plane was established, including both shear and cap components. A nonlinear dependence of the friction angle on mean stress was identified and described by a logarithmic function. The law of plastic hardening was determined, and a non-associated plastic flow rule was confirmed in the shear domain. The calibrated SRP model allows for predicting the backfill mass behavior with high reliability, which is a necessary condition for substantiating the parameters of safe potash mining. Full article

Adaptive and adverse brain states are often assumed to lie on a shared molecular continuum, but this assumption has rarely been evaluated against explicit transcriptomic alternatives. This study aimed to compare two representations of cross-context brain transcriptomic organization: a transcriptome-wide global-axis model and a low-dimensional reciprocal model. We benchmarked these models across a curated cross-study brain cohort spanning exercise, alcohol-related adversity-like contexts, stress, aging, and neurodegeneration, using prespecified intervention-like and adversity-like directional contrast labels rather than assuming homogeneous biological states. We assessed the competing representations using signed-effect correlations, permutation analyses, non-linear fitting, and held-out reconstruction, and we then examined the resulting structure through region-specific human bulk evaluation and exploratory cellular, single-nucleus, spatial, and chromatin projection analyses. These downstream analyses were used to examine localization and biological interpretability and were not treated as independent evaluation of the module 1/module 2 (M1/M2) partition. The combined signed-effect statistics were interpreted as representation-level directional summaries rather than estimates of a homogeneous cross-study biological effect. The global-axis model received limited support: intervention-like and adversity-like signed-effect summaries were only weakly correlated, were not stronger than permutation null expectations, and were not improved by non-linear fitting. Within the selected reciprocal-gene space, a rank-1 latent profile reconstructed held-out genes more accurately than the hard M1/M2 partition, whereas the M1/M2 discretization provided a more interpretable but selection-conditioned directional summary. Human analyses yielded an asymmetric pattern: a significant M1 association was observed only in the hippocampal dataset, whereas M2, the reciprocal index, and the other examined brain regions showed no consistent corresponding effects; leave-one-stratum-out analyses indicated poor cross-stratum reproducibility of the exact gene-level partition. These findings motivate a low-dimensional reciprocal representation as an exploratory framework while emphasizing context dependence, cohort dependence, and heterogeneity. Full article