Search Results (122)

Numerous studies have demonstrated that climate exerts a substantial impact on forest growth. Traditional stand growth yield tables fail to take climatic factors into account, resulting in low accuracy and limited applicability. The study aimed to develop stand growth tables under climate change scenarios by incorporating the effects of climatic factors on stand growth, based on five successive permanent plot monitoring of natural Quercus mongolica broad-leaved mixed forests in Jilin Province from 1994 to 2014. First, the optimal growth models were selected from common theoretical growth equations to establish the site class curve. Then, growth equations for stand quadratic mean diameter at breast height (Dg), number of trees per hectare (N), and stand volume per hectare (V) were fitted separately. Climate-sensitive stand growth tables were compiled by site class using the simultaneous equations method, and stand growth dynamics were predicted under four climate scenarios (SSP126, SSP245, SSP370, SSP585) from 2011 to 2100. The results showed that model fitting identified the Logistic function as optimal for stand mean height and Dg, an exponential function for N, and the Richards function for V. Stand mean height was positively correlated with mean annual precipitation. Dg was positively correlated with mean temperature of the warmest month and mean annual precipitation. N was negatively correlated with mean temperature of the warmest month and showed no significant correlation with mean annual precipitation. V was negatively correlated with mean temperature of the warmest month and positively correlated with mean annual precipitation. The climate-sensitive stand growth tables improved long-term prediction accuracy compared with conventional static yield tables. This study provides dynamic and reliable technical support for the scientific management and ecological regulation of Quercus mongolica forests under climate change, and it also offers methodological references for compiling growth tables of other broad-leaved mixed forests. Full article

Reliable intensity–duration–frequency (IDF) curves are essential for the design of stormwater drainage systems and flood mitigation infrastructure; however, conventional IDF relationships assume stationarity and may underestimate future rainfall extremes under climate change. This study developed climate-informed IDF curves for Makkah, Saudi Arabia, using hourly observed rainfall records (1985–2025) and projections from five CMIP6 Global Climate Models (EC-Earth3-CC, CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-LR, and UKESM1-0-LL) under the SSP245 and SSP585 scenarios. Spatial downscaling was first carried out using bilinear interpolation, after which the resulting data were corrected for systematic bias using the Delta Change method. Daily precipitation projections were subsequently disaggregated to an hourly timescale using an enhanced KNN-MOF approach. Annual maximum precipitation series were then derived for durations of 1, 2, 3, 6, 12, and 24 h and fitted to a range of candidate probability distributions. The goodness of fit was evaluated using the log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Across the five CMIP6 models, two emission scenarios, and six rainfall durations, the Log-Pearson Type III distribution consistently yielded the most satisfactory fit. Historical analysis estimated 100-year rainfall depths ranging from 7.84 mm (1 h) to 38.29 mm (24 h), while future projections indicated substantially higher design rainfall intensities under several climate models. For example, under the SSP585 scenario, the 100-year 1 h rainfall intensity reached 29.73 mm h⁻¹ for EC-Earth3-CC, whereas MPI-ESM1-2-LR projected a 102% increase in the 6 h 100-year intensity relative to SSP245. Sherman equations were successfully fitted to develop continuous IDF relationships, while bootstrap resampling and Bayesian inference quantified projection uncertainty. The multi-model ensemble indicated increasing uncertainty with return period, particularly for the 100-year event, highlighting the importance of incorporating uncertainty into engineering design. The proposed framework provides robust climate-informed IDF curves for supporting resilient urban drainage design, flood-risk assessment, and water resources planning in arid environments. Full article

Wind power producers face severe financial risks from intermittent generation and volatile prices. In this paper, we develop a multivariate scenario-based optimization framework that integrates heavy-tailed Laplace forecast-error path generation, principal component (PC) score matching, and Frequency-Concentrated Projection (FCP) to determine day-ahead bidding curves that maximize expected settlements. To model uncertainty, 24-h scenarios are generated by sequentially accumulating heavy-tailed Laplace forecast-error increments. Trajectories of specific variables are integrated via PC-score matching to construct joint scenarios preserving inter-variable dependencies. A dense optimal response derived from these scenarios is compressed into a market-compatible 11-point bidding curve using FCP, which strategically allocates submission points to highly probable clearing intervals. Evaluation on 2021 NYISO West data demonstrates substantial improvements in both feasibility of scenarios and financial performance. The Laplace specification captures extreme price spikes, so it significantly reduces downside risk compared to a Gaussian baseline. PC-score matching ensures feasibility of structure by preserving daily trajectory shapes. Leveraging these robust scenarios, the FCP curve yields substantially higher realized settlements than the Uniform Support Baseline (USB), which uniformly places the limited submission points across the price range, recovering approximately 90% of the settlement gap between USB and the Dense Optimal Response (DOR), which serves as a non-submittable dense-grid upper-bound benchmark. Ultimately, this framework translates complex uncertainty models into actionable strategies, enabling producers to systematically maximize economic returns under rigid market constraints. Full article

This research examined whether battery energy storage could economically utilise event-conditioned curtailed wind energy by optimally allocating stored electricity between wholesale grid export and EV charging. The analysis focused on a 43.2 MW onshore wind farm in Central Greece. Expected non-curtailed generation was estimated from ERA5 100 m wind data and the Vestas V117 power curve, calibrated against measured non-event production, and compared with measured output during IPTO-reported curtailment-event hours. The estimated recoverable curtailed energy was treated as the sole BESS charging source, and three scenarios were evaluated: grid-only discharge, EV-only discharge, and joint grid-plus-EV allocation. The dispatch and sizing problem was formulated as an MILP-based, NPV-oriented optimisation including an explicit no-investment alternative and was assessed over a 20-year FCFE horizon using Monte Carlo simulation and sensitivity analyses. The calibrated model reduced holdout nRMSE from 21.92% to 17.83% and mean bias from −4.309 to 0.117 MWh. Under the base scenario, 1537 event hours yielded positive estimated curtailment, corresponding to 10.26 GWh. The no-investment option was the global NPV-maximising solution in all baseline scenarios. The best non-zero BESS Pmax/Emax was 0.25 MW/0.5 MWh, with expected NPVs of −332.6 k€, −356.3 k€, and −321.0 k€ for grid-only, EV-only, and grid-plus-EV operation, respectively. EV-premium breakeven required 1207.5 €/MWh for EV-only and 1186.4 €/MWh for grid-plus-EV operation. The results show that market diversification improves curtailed-energy utilisation, but curtailed-energy-only BESS investment remains financially unattractive under the observed conditions. Full article

Background: Thyroid function tests are frequently requested, but manufacturer reference intervals often lack age- or sex-based stratification. This study established indirect reference intervals for thyroid stimulating hormone (TSH), free thyroxine (fT4) and free triiodothyronine (fT3) in a large adult population. We evaluated unsupervised algorithms and conventional partitioning to identify subgroups, compared multiple limit estimation methods, and assessed the diagnostic performance of the derived intervals against an independent, clinically defined external validation cohort. Methods: Data from 37,255 adults (age ≥ 18) collected between 2022 and 2024 were analyzed; a reference population of 5870 individuals was established following standardized exclusion criteria. Age-based subgroups were identified through hierarchical clustering with the Elbow method, and variable importance was assessed using Random Forest analysis. Reference intervals were calculated using non-parametric, Bhattacharya, refineR, and reflimR algorithms, applied to three population frameworks: (1) the total population without stratification, (2) subgroups derived from hierarchical clustering and (3) subgroups defined by the conventional Harris–Boyd partitioning method. Diagnostic performance was subsequently evaluated in an independent external cohort (National Health and Nutrition Examination Survey [NHANES]; N = 2297) for all estimated reference intervals. Three classification scenarios were assessed: TSH-only, fT4-only, and combined TSH + fT4, with sensitivity, specificity, Youden index, and decision curve analysis performed for each. Results: Random Forest analysis identified age as the dominant variable influencing TSH, fT4 and fT3 distributions (mean decrease in accuracy: TSH 41.62, fT4 44.18, fT3 43.7), while sex showed the lowest impact. Clustering yielded six age-based subgroups for analytes. Harris–Boyd partitioning yielded six age-based subgroups for TSH, two sex-based subgroups for fT4, and six combined age-and-sex subgroups for fT3. TSH limits were broadly concordant across all three approaches (six-subgroup partitioning: 0.36–0.68 to 4.75–5.67 mIU/L). For fT4, conventional (sex-based) and clustering (age-based) partitioning produced similar ranges (11.33–20.08 pmol/L), except reflimR’s notably lower limit (10.90 pmol/L). For fT3, conventional (age + sex) and clustering (age-only) partitioning showed comparable ranges (3.36–7.03 pmol/L), with clustering revealing a clearer age-related decline in the oldest group. Diagnostic performance varied markedly by analyte. TSH-only classification achieved positive discrimination across all 13 methods (Youden index: 0.173–0.239). In contrast, fT4-only and combined TSH + fT4 classifications performed at or below chance for most methods, with 75% of the cohort falling outside fT4 reference intervals, indicating an inter-platform harmonization issue rather than a partitioning failure. Decision curve analysis confirmed TSH-only classification’s superiority, exceeding universal testing from pt ≈ 0.20 onward across all methods. Conclusions: Age-stratified reference intervals combined with limit estimation showed potential diagnostic advantages over manufacturer and non-stratified intervals; however, an independent external validation using a clinically defined outcome indicated that this advantage was not consistently reproduced and was dependent on the clinical decision threshold considered. These results suggest that age stratification and algorithm choice merit further clinically adjudicated validation before broad clinical adoption, and that unsupervised clustering offers a practical, objective alternative to manual subgrouping for laboratories pursuing this approach. Full article

The transition to a decarbonized energy infrastructure relies on repurposing existing pipelines for hydrogen–methane mixtures, which introduces significant concerns regarding hydrogen embrittlement. Accordingly, a coupled Multiphysics phase-field model was developed to predict hydrogen-assisted failure in elastic–plastic solids. This framework is numerically implemented via the finite element method to predict the structural integrity of pipeline steel strength classes representative of API 5L X65, X70, and X80 by explicitly accounting for elastoplastic deformation, hydrogen trapping effects, and stress-driven diffusion. By computing crack growth resistance curves across various scenarios, it has been demonstrated the capability of the model to capture material sensitivities by varying hydrogen–methane blend compositions, operational pressures, and the elastoplastic deformation behavior of different strength grades. The investigation revealed that methane limits surface hydrogen coverage, thereby mitigating the crack-tip decohesion mechanism. Furthermore, the model indicates that at a pressure of 7.5 MPa, a 15 vol% hydrogen–methane blend enables these materials to retain 80–90% of their fracture toughness and exhibit ductile failure. Finally, higher-strength steel classes (representative of X80) demonstrate greater susceptibility to hydrogen embrittlement under these conditions due to yield stress-amplified hydrostatic stress, whereas lower-strength steels exhibit greater defect tolerance for the hydrogen-blend transition. Full article

Evaluating whether clinical classifiers perform equitably across patient subgroups is a central requirement for the responsible deployment of machine learning in medicine. Conventional approaches test one fairness metric at a time, such as sensitivity, positive predictive value, or area under the receiver operating characteristic curve, and therefore cannot detect disparities that manifest only in the joint structure of a group-specific confusion matrix. We develop a unified hypothesis-testing framework rooted in random matrix theory that compares demographic groups through the $L^2$ distance between their joint eigenvalue densities, yielding a scalar spectral divergence that is sensitive to every cell of the $2\times 2$ confusion matrix simultaneously. We derive the closed-form spectral divergence for Gaussian-approximated eigenvalue densities, prove almost-sure consistency of the empirical estimator via the delta method, and construct an extreme-value (Gumbel) test statistic with family-wise error rate control. Monte Carlo experiments comprising $\mathrm{10,000}$ replications across balanced, moderately imbalanced, and severely imbalanced group-size regimes show that the spectral test keeps Type I errors close to its nominal level while achieving power exceeding $90\%$ in complex and multi-dimensional violation scenarios, where the best single-metric competitor reaches at most $63\%$. Three clinical benchmark datasets from the UCI Machine Learning Repository utilised include Pima Indians Diabetes ($n=768$), Cleveland Heart Disease ($n=303$), and Heart Failure Clinical Records ($n=299$). Results confirm that the spectral method detects statistically significant ($p<0.001$) performance disparities missed by all three conventional tests. These results support eigenvalue-based divergence as a practical, model-agnostic diagnostic equity tool for clinical machine learning audits. Full article

To elucidate the active–passive reinforcement mechanisms of rock bolts and the evolution of shear strength in anchored rock joints, this study integrates theoretical analysis, laboratory direct shear tests, and numerical simulations to investigate the deformation and failure characteristics of fully grouted, end-anchored, and prestressed bolted specimens. The results show that bolt reinforcement can be classified into prestress-dominated active action and dislocation-induced passive action. The shear strength curve of anchored rock joints exhibits four distinct stages with increasing shear displacement: initial slip, elasticity, yielding, and softening. Fully grouted bolts fail primarily by tensile–shear fracture, enabling a rapid increase in shear strength at small displacements. In contrast, end-anchored bolts undergo S-shaped bending and form symmetrical plastic hinges on both sides of the joint, sustaining resistance under large displacements albeit with lower peak strength. While the laboratory tests experimentally clarified the distinct failure modes and passive shear resistance mechanisms of fully grouted and end-anchored bolts, the quantitative partitioning between active and passive contributions was derived from a numerically simulated prestressed bolt model. The simulations indicate that for prestressed bolts, the active contribution accounts for approximately 69.6% of the total shear strength enhancement, while the passive contribution is about 30.4%. These findings yield actionable design criteria: end-anchored or yielding bolts are recommended for high-geostress environments or scenarios involving large potential deformations to exploit the large-deformation bearing capacity of passive action; conversely, prestressed bolts should be prioritized where strict control of early-stage deformation is required to maximize active support efficiency. Full article

Accurate estimation of the Doppler centroid is a prerequisite for achieving high-quality Synthetic Aperture Radar imaging. In highly squinted working scenarios, traditional frequency-domain methods depend on antenna pattern fitting. They are easily affected by pattern mismatch and strong scatterer interference. In addition, these methods cannot directly determine the Doppler ambiguity number. The range envelope-based Hough transform can correct linear range walk. It further realizes Doppler centroid estimation without ambiguity. However, range curvature hinders its estimation accuracy. To solve the above problem, this paper proposes a novel squint angle estimation scheme. The scheme organically combines closed-loop iterative range curvature compensation and energy-weighted Hough transform. Within a closed-loop iterative architecture comprising curvature compensation, line feature extraction, direction measurement, and angle refinement, the presented method progressively rectifies curved trajectories, yields robust squint angle estimates, and further derives the unambiguous Doppler centroid indirectly. Both simulated datasets and real airborne SAR measurements demonstrate the effectiveness and robustness of the proposed method. Full article

Microscopic traffic simulation is widely used to evaluate traffic operations in continuous-flow tunnel scenarios. However, conventional calibration methods mainly rely on aggregate indicators such as average speed or traffic flow. Under constrained geometric conditions, stable lane-use patterns, and mixed passenger car and truck operations, different parameter combinations may reproduce similar macroscopic traffic states while generating different car-following behaviors. Therefore, aggregate-indicator-based calibration alone cannot ensure behavioral realism. The principal contribution of this study is a stepwise macro- and mesoscopic calibration framework that first constrains car-following behavior using the Speed Gap Function (SGF) and then refines the macroscopic traffic state using the Speed Distribution Function (SDF). The SGF characterizes the relationship between vehicle speed and net spacing, thereby capturing longitudinal interactions often overlooked in conventional calibration, whereas SDF describes the cumulative speed distribution. Latin Hypercube Sampling and VISSIM batch simulations are used to generate a dataset for 19 driving behavior parameters, and multilayer perceptron surrogate models are trained to improve optimization efficiency. Single-objective, simultaneous multi-objective, and stepwise calibration schemes are compared. The SGF-priority stepwise scheme achieves the most balanced performance, with SGF and SDF MAPE values of 12.00% and 11.53%, respectively, corresponding to average relative discrepancies of approximately 12% in reproducing the two calibration curves. An independent capacity pressure test used for external validation yields a deviation of only −1.84%, indicating that the simulated capacity is within 2% of the reference value. Overall, the proposed framework improves behavioral consistency and engineering applicability under high-demand tunnel conditions. Full article

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

Background/Objectives: In gas chromatography–mass spectrometry (GC-MS) library-based compound identification, spectrum preprocessing and associated tuning parameters critically influence identification performance. These parameters are conventionally optimized using grid search, which requires predefined parameter spaces and becomes computationally inefficient as dimensionality increases, often failing to identify optimal values because of discretization. Differential evolution (DE), a population-based metaheuristic optimization algorithm, provides a flexible alternative through efficient global exploration of the parameter space. This study compared the performance of DE and grid search for optimizing compound identification. Methods: Cosine similarity was applied to the NIST GC-MS library. DE was used to maximize either cross-validated accuracy or mean reciprocal rank (MRR). Results were compared with those from a grid search over five equally spaced parameter values. Identification performance was evaluated using accuracy, MRR, and area under the receiver operating characteristic curve (AUC). Results: When all four parameters were optimized simultaneously, DE achieved slightly higher cross-validated accuracy and MRR than grid search, although the absolute differences were modest. More pronounced differences were observed in specific unidimensional tuning scenarios, particularly for the intensity weight factor. Simultaneous multidimensional parameter optimization yielded better performance than isolated parameter tuning. Conclusions: Grid search may be computationally advantageous when the parameter space is known and limited, whereas DE provides a more flexible approach for unknown or high-dimensional search spaces. Overall, DE achieved comparable identification performance to grid search, with modest improvements observed in some optimization settings. A command line Julia-based tool, MSTune, was developed for spectrum preprocessing parameter optimization and is publicly available on GitHub. Full article

Background: Heterogeneous data integration remains a major challenge in intelligent information systems, particularly under missing-modality and cross-domain conditions. Existing multimodal fusion approaches often rely on complete datasets and weak alignment mechanisms, limiting their robustness and practical applicability. Objectives: This study aims to develop and evaluate a genomics-guided multimodal representation learning framework that enables robust heterogeneous data fusion, reliable cross-modal correspondence, and accurate prediction under incomplete-data conditions. Methods: We propose a multimodal learning architecture that models genomics as the primary biological anchor and learns conditional projections to imaging modalities, including multiparametric MRI and whole-slide histopathology (WSI). The framework formulates multimodal fusion as a genomics-guided contrastive learning problem, incorporates domain-specific optimization constraints, and learns a latent shared-state representation to support inference without requiring fully paired datasets. Evaluation was conducted using public datasets, including TCGA-PRAD and TCIA, across low-risk versus higher-risk/clinically significant prostate cancer (csPCa) discrimination, Gleason-based risk stratification, and clinically significant outcome prediction tasks under realistic multimodal and missing-modality scenarios. Results: In the adequately powered $\mathrm{G}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{s}+\mathrm{W}\mathrm{S}\mathrm{I}$ cohort (n = 486), the framework achieved an AUROC of 0.985 ± 0.005 for low-risk versus higher-risk/csPCa discrimination (p < 0.001). Exploratory analysis in a small, matched $\mathrm{G}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{s}+\mathrm{M}\mathrm{R}\mathrm{I}$ cohort (n = 28) yielded an AUROC of 0.980 ± 0.006 for the same endpoint; these findings are reported descriptively with bootstrap confidence intervals due to limited sample size. Because the negative reference group consisted of low-risk prostate cancer cases rather than cancer-free controls, results are interpreted as within-cancer risk discrimination rather than de novo cancer detection. The framework achieved weighted accuracy up to 92.1%, Cohen’s κ up to 0.86, and reduced critical decision errors by 58%. Calibration remained strong (ECE 0.021–0.024), and decision-curve analysis indicated improved utility with reduced unnecessary invasive workups in retrospective modeling. Robustness analysis demonstrated AUROC degradation below 0.04 under domain shifts. Single-modality inference using genomics alone maintained AUROC > 0.90. Interpretability analysis revealed feature attributions aligned with domain-relevant genomic markers. Conclusions: The proposed framework provides a scalable and generalizable solution for heterogeneous multimodal data fusion, supporting reliable prediction, robustness to missing modalities, and applicability to complex information systems beyond the studied domain. Full article

Coverage optimization of wireless sensor networks (WSNs) faces challenges such as uneven node distribution and vulnerability to coverage blind spots. This paper introduces and improves the Northern Goshawk Optimization (NGO) algorithm: the Logistic chaotic map is adopted to initialize the population for enhanced ergodicity, a nonlinear dynamic weight is introduced to balance global exploration and local exploitation, and a Gaussian–Lévy hybrid mutation mechanism is integrated to strengthen the ability to escape from local optima. Experiments on standard test functions show that the improved algorithm (INGO) can stably approach the theoretical optimal values for both unimodal and multimodal functions. The convergence speed and solution accuracy are significantly superior to those of the original NGO, with a smaller standard deviation and stronger robustness. INGO is applied to the coverage optimization of 2D and 3D WSNs, with coverage rate as the fitness function, and the optimal node deployment coordinates are output through iterative optimization. Simulation results show that INGO achieves a best coverage rate of 98.32% in the 2D scenario, which is 7.72 percentage points higher than the 90.6% of NGO. In the 3D scenario, the best coverage rate reaches 72.32%, 6.78 percentage points higher than the 65.54% of NGO. Meanwhile, INGO yields more uniform node deployment and effectively reduces coverage blind spots. Its convergence curve is smooth and oscillation-free in the late iteration stage, and the stability is significantly better than that of NGO. With proper settings of population size and iteration times, INGO can achieve better coverage performance, providing a reliable technical solution for the efficient deployment of wireless sensor networks in complex environments. Full article

In organizational cyber crises, incident response and official communication form coupled control loops, yet they are usually engineered separately. We present V-CHIMERA (Verified Coupled Human–Information–Machine Incident Response Architecture), a framework for organizational cyber crisis response under misinformation that jointly models cyber state, belief dynamics, trust, and communication governance. The framework combines three elements: an explicit cyber–social coupling architecture, a runtime protocol shield for communication safety, and immune-gated coupling (IGC) that uses danger signaling, tolerance thresholds, and immune memory to regulate when social feedback should affect operational response and how strongly counter-messaging should be targeted. Across three representative scenarios—ransomware rumor, outage rumor, and exfiltration scam—and eight seeds per scenario, all shielded policies achieved zero executed protocol violations. Relative to naive coupled control, IGC reduced cyber-harm area under the curve (AUC) by 57.6% in ransomware rumor and 42.6% in outage rumor while also reducing misbelief. Results were scenario-dependent rather than uniformly dominant: in exfiltration scam, a broadcast-only ablation outperformed targeted messaging, showing that targeting can fail when diffusion rapidly crosses community boundaries. Sensitivity analysis further shows that IGC attenuates the brittleness observed under strong coupling and weak moderation. The results suggest that biomimetic regulation is valuable not because coupling always helps, but because it prevents overreaction, clarifies when targeting should be used, and yields safer organizational defaults for misinformation-aware incident response. Full article