Search Results (4,379)

Due to the increase in the number of ontologies in various domains, ranking them to facilitate their selection for reuse is an important task in ontology engineering to date. To assess the multi-faceted quality configurations of candidate ontologies, Multi-Criteria Decision Making (MCDM) frameworks are used. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an MCDM method that is widely adopted for the task of ontology ranking. However, traditional TOPSIS implementations rely almost exclusively on the Euclidean distance metric. This introduces severe rank volatilities and systematic biases when evaluating heterogeneous ontology metadata. To address these limitations, this paper introduces a novel Multi-Distance Ensemble TOPSIS (Ensemble-TOPSIS) method for robust ontology ranking. Rather than forcing a localized geometric choice, the proposed Ensemble-TOPSIS method simultaneously projects alternative ontologies through a multi-distance ensemble composed of Euclidean, Chebyshev, cosine, and Mahalanobis configurations. The Ensemble-TOPSIS method was applied to three datasets of ontologies from the artificial intelligence, agricultural, and biological domains to test its scalability and multi-domain applicability. The experimental results reveal that all the ontologies from the three domains were successfully ranked by the proposed Ensemble-TOPSIS method. Furthermore, the statistical rank correlation using Spearman’s $\rho$, Kendall’s $\tau$, and the $WS$ rank similarity coefficients was calculated between the TOPSIS variants, and the proposed Ensemble-TOPSIS method achieved the highest correlation in the majority of cases. Moreover, a comprehensive Monte Carlo simulation across 1200 stochastically generated, non-linear, and skewed multicollinear decision domains established the asymptotic stability of the proposed Ensemble-TOPSIS method, which achieved the highest global mean performance ($\overline{\rho }=0.88$, $\overline{\tau }=0.75$, $\overline{WS}=0.94$), minimized rank variance (${\sigma }^2\left(WS\right)=0.0006$), and optimally maximized the lower-bound worst-case performance profile ($\rho =0.67$) compared to individual baseline formulations. Full article

State-level health resource systems need precise and timely prediction models, but they are plagued by the ongoing problem of deteriorating model performance because of constantly shifting data distributions (data drift). Predicting uncommon but important events like sickle cell crisis is a classification task where this problem is most noticeable. This paper presents the Evolving AI-Driven Ensemble Learning Framework, which blends novelty detection using the F1-score with sophisticated ensemble approaches (stacking XGBoost, Deep Neural Network, and Random Forest with a meta-learner). Complex, high-dimensional health data are handled using sophisticated feature engineering techniques, such as automated feature selection via evolutionary algorithms and meta-learning (MAML). We empirically assessed a reactive retraining technique that was improved by ensemble stacking and simulated real-time data drift. After five retraining cycles, the improved ensemble and feature engineering showed significant performance improvements over the initial model, achieving substantial improvements: F1-score improved from 0.1250 to 0.9734 (an absolute increase of 0.8484, representing a 678.7% relative improvement), recall from 0.0714 to 0.9767 (an absolute increase of 0.9053), and precision from 0.5000 to 0.9702 (an absolute increase of 0.4702). The framework maintained high specificity (0.9700) and demonstrated outstanding discriminative performance with an AUC-ROC of 0.9909 (an 8.5% improvement). The model’s strong predictive capacity was confirmed by improvements in the Matthews Correlation Coefficient from 0.1000 to 0.9467 (846.7% improvement) and Cohen’s Kappa from 0.0800 to 0.9467 (1083.3% improvement). Model transparency in pipeline development is now made possible by a fixed runtime issued in the SHAP explainability layer. The efficiency of the framework is empirically validated by this study, showing that automated feature engineering and optimized ensemble learning greatly increase model stability and preserve remarkable accuracy for minority classes in complicated data contexts. Full article

This study introduces a training-free, self-contained adaptive low-light image enhancement (LLIE) framework driven by a metaheuristic optimization algorithm (MOA) and a context-aware dual image quality assessment (IQA) engine. Although deep-learning-based methods exhibit rapid inference, their static parameters often suffer from severe performance degradation in out-of-distribution (OOD) scenarios—such as those involving unseen sensor noise or environmental shifts. To bridge this generalization gap, the proposed framework operates within a decoupled HSV color space, specifically targeting the luminance ($V$) channel to formulate image enhancement as an instance-specific optimization task. We introduce a novel hybrid Log-Gamma mapping function that mathematically unifies the localized dark-stretching capabilities of logarithmic compression with the global dynamic range regulation of power-law gamma curves, thereby substantially expanding the expressiveness of the transformation space. To govern parameter convergence without reference images, a multi-stage Low-Light Image Discrimination (LLID) engine classifies the input frame by computing context-specific trimmed skewness residuals and global intensity means, effectively mitigating highlight biases. Under normal-light conditions, the swarm intelligence engine optimizes the Log-Gamma coefficients via the Patch-based Contrast Quality Index (PCQI) to maximize structural fidelity; conversely, under severe low-light degradations, the framework leverages the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) to minimize spatial artifacts. Using the Marine Predators Algorithm (MPA), the framework iteratively searches the continuous bounding space to fine-tune a parameter matrix tailored exclusively to each image. Empirical evaluations across four benchmark datasets (Bicycle, DF1000, DICM, and VV) validate the effectiveness of the proposed paradigm. The proposed variant, $OLG\left(\mathrm{MPA}\right)$, secured the top average rank in internal algorithm ablation ($\overline{R}=3.10$) and achieved a competitive global average rank ($\overline{R}=2.95$) against four state-of-the-art deep networks, matching the performance of leading data-driven models. Although the evolutionary optimization loop incurs an average per-frame latency of $17.300 \mathrm{s}$, this instance-specific paradigm successfully trades instantaneous processing speed for absolute domain adaptability and predictable, artifact-free image restoration. Full article

Formation pressure labels are often stored as equivalent mud weight (EMW), whereas engineering analysis requires true pressure and validation strategies that account for the strong depth dependence of borehole data. This study develops a dimensionally consistent pressure ratio symbolic regression framework in which vertical overburden stress provides the pressure scale and a compact dimensionless correction is learned from reference-scaled borehole variables. After converting EMW-based labels to MPa and applying transparent quality control criteria, 1877 of 2025 depth-indexed records were retained. Redundant and deterministically derived variables were identified through correlation analysis, variance inflation factors, principal component analysis, and deterministic relation checks and were excluded from the compact symbolic search. Five contiguous depth blocks with a 20 m purge zone were used for leakage-resistant validation, with symbolic-structure selection repeated within each outer training fold. The selected four-coefficient expression, comprising an intercept and three predictor-dependent terms, retained only depth and acoustic slowness. Fixed-structure depth-block validation yielded a coefficient of determination (R²) of 0.3008, a root mean square error (RMSE) of 3.5439 MPa, a mean absolute error (MAE) of 2.6964 MPa, and a mean absolute relative error (MARE) of 10.02%, while the conservative nested symbolic pipeline yielded MARE = 11.60%. External zero-shot evaluation on 974 samples from an external well, 98.36% of which were outside the calibration depth range, yielded R² = 0.0265, RMSE = 7.0024 MPa, MAE = 5.5723 MPa, and MARE = 11.51% against an independent D-exponent-based engineering pressure reference (PP_DX). Sparse local recalibration using 10 depth-spaced PP_DX engineering reference points, with ±20 m neighborhoods excluded from evaluation, improved performance to R² = 0.8684, RMSE = 2.8431 MPa, MAE = 2.1108 MPa, and MARE = 4.39% on the remaining 564 samples. These results indicate that the proposed framework provides an explicit and independently auditable pressure equation that preserves the overall pressure scale under substantial depth domain shift and can be efficiently recalibrated using sparse local pressure information. Site-specific calibration remains necessary for accurate reproduction of local pressure variations. Full article

Single-criterion feature selection for hyperspectral crop yield estimation suffers from methodological bias and limited generalisation. This study proposes a stacking-fusion feature selection framework with a ridge-regression meta-learner (STACKING_FUSION) that transfers the stacked-generalisation concept to the feature-evaluation space, integrating the Pearson correlation coefficient (PCC), grey relational analysis (GRA), and variable importance in projection (VIP) and using the out-of-fold $R^2$ as a meta-supervision signal for adaptive weighting. In field experiments (2024–2025, Gongzhuling, Jilin Province) on rice cultivars Jijing 830 and Jijing 855, leaf hyperspectral reflectance (400–2400 nm) was acquired under controlled indoor measurement conditions at the tillering, jointing, flowering, and milking stages; the study was thus conducted at the leaf scale rather than at the canopy scale of UAV or satellite remote sensing. Second-derivative spectra outperformed original and first-derivative spectra at most stages, and STACKING_FUSION with XGBoost achieved the highest accuracy ($R^2$ = 0.948, RMSE = 0.018 kg m⁻², ratio of performance to deviation (RPD) = 4.399). Joint interpretation using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), computed on a held-out test subset, identified flowering-stage indices as the leading predictors within the model and suggested a flowering–tillering cross-stage association. With the configuration fixed from the 2024 development dataset, the 2025 analysis showed that the framework was reusable as a locked feature-engineering and algorithmic configuration rather than as a directly portable fitted predictor: under strict zero-shot application it retained high predicted–measured correlations (Pearson r≈ 0.91–0.93) but showed a consistent negative bias together with additional scale and residual error, whereas recalibration on a target-domain calibration subset (70% of the 2025 samples) achieved RPD > 3.0 in both the cross-year and cross-cultivar evaluations. These results indicate that the reusable component is the locked feature-engineering and algorithmic setting rather than the fitted coefficients. Full article

Reliable estimation of liquid accumulation in gas-well tubing is important for characterizing liquid-loading conditions and supporting engineering assessment. Traditional mechanistic approaches commonly depend on an extensive set of wellbore descriptors and empirical parameters, while also requiring complicated solution procedures. This work addresses these constraints through a data-driven predictive framework that couples ensemble feature selection with ant-colony-optimized support vector regression (ACO-SVR). A majority-voting scheme was applied to 107 production-test records collected from a gas field. The scheme combined linear regression, grey relational analysis, random-forest mean decrease in impurity, the Pearson correlation coefficient, and SHAP attribution, and selected seven dominant factors from 11 candidate variables: casing pressure, tubing pressure, tubing depth, reservoir mid-depth, daily gas production, daily water production, and wellhead temperature. Ant colony optimization subsequently determined the SVR hyperparameters. Evaluation with 32 held-out well samples produced a root-mean-square error of 165.73 m, a mean absolute error of 103.26 m, a coefficient of determination of 0.94, and a mean relative error of 2.11%. Repeated five-fold cross-validation further yielded an average $R^2$ of $0.91\pm 0.04$ and an RMSE of $181.6\pm 24.8$ m, indicating moderate variability across alternative data partitions. Relative to the untuned SVR, ACO-SVR lowered the root-mean-square error and mean absolute error by approximately 27.0% and 31.1%, respectively. Its mean relative error was also 3.66 percentage points below that of the PLATA model. The resulting framework provides accurate prediction of tubing liquid accumulation height from a small sample and offers quantitative information for liquid-loading assessment under the investigated operating conditions. Full article

This paper examines how circular economy (CE) policies relate to sustainable economic growth and environmental sustainability across eight major emerging economies between 2005 and 2023, with particular attention to how that relationship shifts across time horizons. Much of the existing literature has a temporal aggregation problem: short- and long-run dynamics are averaged together into a single coefficient, which can obscure the underlying relationship or, worse, produce a misleading sign. We address this by decomposing two central CE variables—renewable energy share and energy intensity—into short-, medium-, and long-term frequency components using a Level-2 discrete wavelet transform. Each component is then estimated via two-way fixed-effects panel regressions; given the constraints of a relatively small panel, we validate inference using a Wild Cluster Bootstrap procedure. Three findings stand out. First, although the growth elasticity of renewable energy rises across horizons—a descriptive pattern consistent with short-run adjustment costs giving way to longer-run sustainable gains—Wild Cluster Bootstrap (WCB) inference indicates that these structural coefficients are not statistically significant in this sample. Second, the energy intensity coefficient reverses the sign, moving from a weakly significant negative short-run effect (p < 0.10) to positive estimates in the longer run; these longer-run effects, however, fail to reach statistical significance, and so offer only suggestive theoretical alignment with the macroeconomic Jevons Rebound Effect rather than firm empirical confirmation of it. Third, Industry Value Added stands out as the one robust macroeconomic factor in the model, remaining a significant and positive driver of sustainable development at every frequency horizon. Taken together, these results suggest that while CE transitions display suggestive multi-horizon dynamics, industrial expansion remains the more statistically dependable engine of sustainable growth across these emerging markets. Although statistical significance is dependent on the sample size, the identified trajectories provide a useful structural diagnosis for policymakers who need to plan different investment horizons for the sustainable green transition. Full article

Sudden collapse accidents in mine roadways occur frequently, and post-disaster emergency rescue faces major challenges in terms of safety and efficiency. Therefore, efficient demolition equipment and intelligent prediction methods are urgently needed. Abrasive waterjet technology has considerable potential for complex disaster environments owing to its high efficiency, environmental friendliness, and cold-cutting characteristics. However, its cutting performance is affected by multiple coupled factors, including jet parameters, material properties, and environmental conditions. This makes accurate prediction difficult, especially under extreme or unseen operating conditions where available samples are limited. To address this problem, this study proposes a zero-shot learning-based multi-physics coupling prediction framework for the “jet–material–environment–effect” relationship. The framework is designed to predict abrasive waterjet cutting performance under unseen working conditions. First, a multi-factor cutting-performance dataset is constructed through a hierarchical experimental design. A generative adversarial network (GAN) is then introduced to expand the sample space and compensate for the discrete nature and limited distributional coverage of the experimental data. Second, a lightweight self-attention mechanism is employed to model high-dimensional input features globally, thereby improving the model’s ability to capture complex feature interactions. Finally, a joint loss function is designed to collaboratively optimize the generation and prediction processes. The experimental results show that the proposed model achieves a prediction accuracy of 98.3% on the test set, with a coefficient of determination R² of 0.967, outperforming WOA-SVM, BP neural network, EML, and Transformer models. The inference response time is approximately 3.2 s, indicating good engineering applicability. The results demonstrate that GAN effectively expands the sample space and improves model generalization, while the LightTransformer structure provides advantages in modeling high-dimensional coupled inputs. The proposed method can provide theoretical support and technical reference for intelligent demolition rescue and cutting-depth prediction under mine disaster conditions. Full article

To address the delayed strength development and uncertain rock-backfill interfacial stability of total-tailings paste backfill under low-temperature underground conditions, this study aims to quantify the coupled effects of slurry mass concentration, cement/tailing (C/T) ratio, curing temperature, and curing age on uniaxial compressive strength, and to clarify how interface roughness and curing age govern interfacial shear behavior and field strength. A silver–lead–zinc mine in Inner Mongolia was selected as the engineering background, and uniaxial compression, double-sided shear, SEM, and in situ strength tests were conducted. Results show that UCS increased with curing temperature, curing age, slurry mass concentration, and C/T ratio within the investigated ranges, with the comparative influence following the order: C/T ratio > curing age > curing temperature ≈ slurry mass concentration. With age, hydration products increased, pores and microcracks decreased, and structure densified. When joint roughness coefficient (JRC) increased from 0 to 26.76, cohesion rose from 93.31 to 965.57 kPa, and the failure mode shifted from interface slip to backfill shear. Increasing age from 3 to 7d raised cohesion from 611.02 to 965.57 kPa and the internal friction angle from 29.09° to 33.98°. Optimal conditions were 66% concentration and 15 °C, with C/T ratios of 1:4 (adhesive layer) and 1:8 (ordinary layer). Test results under various ratios and ages all indicate that the underground backfill has attained early self-standing and bearing capacity. Full article

Spacecraft and launch-vehicle structures require lightweight multipoint monitoring under combined mechanical, thermal, and environmental loads. This study presents an analytical fiber Bragg grating (FBG) sensor-network workflow integrating reference-grating temperature compensation, regional strain assessment, opposite-surface curvature sensing, wavelength-division-multiplexing allocation, and strain-to-shape reconstruction. The deterministic compensation case is used only as a self-consistency check, whereas practical robustness is assessed through 10,000 Monte Carlo trials incorporating packaged-coefficient mismatch, temperature nonuniformity, wavelength noise, strain-transfer variation, drift, and calibration uncertainty. The calibrated estimator achieved a median strain mean absolute error of 1.73 με and a 95th-percentile error of 4.22 με. The defined finite-element benchmarks produced a maximum engine-mount truss strain of 456.2 με under the defined loads and a median full-field panel-reconstruction normalized root-mean-square error of 1.29% for 18 sensing locations with 2 με noise. Conservative WDM analysis yielded 54, 13, and 16 channels for three operating envelopes, and the prescribed random-vibration spectrum produced 6.78 g_rms. These results demonstrate a reproducible numerical proof of concept and define practical limits for compensation, spectral allocation, curvature interpretation, and inverse reconstruction; they do not constitute experimental validation or flight qualification. Full article

Dye-sensitized solar cells based on Hagfeldt donor sensitizers achieve high open-circuit voltages through effective suppression of interfacial charge recombination. However, their absorption remains largely confined to the visible region, which limits further gains in the photocurrent and overall efficiency. This study addresses the issue of spectral limitation by designing six D-A-π-A organic dyes, HJ101~HJ106. These dyes are derived from the reference sensitizer XY1 through systematic modification of the donor unit with anthracene and squaraine moieties combined with two benzothiadiazole-type auxiliary acceptors. Geometric structures, frontier molecular orbitals, absorption spectra, and excited-state charge transfer characteristics were investigated using density functional theory and time-dependent density functional theory. The results demonstrate that donor and acceptor modifications act synergistically to control the spectral direction and magnitude, with several dyes achieving pronounced redshifts extending into the near-infrared region while retaining thermodynamically favorable electron injection and regeneration driving forces. Among the designed structures, HJ106, which combines a squaraine-modified donor with a redshifting acceptor, exhibits the largest bathochromic shift and an extended excited-state lifetime. HJ105 delivers the highest molar extinction coefficient and light-harvesting efficiency. Collectively, these findings identify squaraine-based donor engineering as the most promising strategy for near-infrared-responsive Hagfeldt-type sensitizers. These findings offer practical structural guidance for the development of next-generation dye-sensitized solar cell sensitizers with broadened spectral coverage. Full article

Public generative artificial intelligence (GenAI)–student datasets invite contested links between AI intensity, usage style, and grades, yet many analyses treat predictive accuracy or significant coefficients as sufficient evidence while skipping prior achievement, co-outcome leakage checks, and absolute effect-size thresholds. This paper presents a construct-audit protocol that treats associational claim survival as a reproducible labeling task: a feature-role taxonomy, forbidden-feature gates, nested out-of-fold change-in-$R^2$ materiality thresholds, and operational labels (stable, vanished, artifact-born—the last defined but not positively observed here), with predictive models used as instruments rather than as the scientific product. On the public ai_student_impact_dataset, treated as a construct-audit sandbox (possibly synthetic or engineered; no campus-population or causal claims), a five-seed Ridge-primary run is used to validate those rules rather than to estimate GenAI effects: all eight primary intensity and style claims are non-material under locked absolute gates (AI joint change-in-$R^2\approx 0.0064$ versus prior grade-point-average lift $\approx 0.859$), while a kitchen-sink OLS significance foil stars $12/19$ coefficients that the inventory does not promote. A report-only Random Forest check shows that style and joint-block clearance can depend on the modeling instrument; inventory labels remain Ridge-primary under the locked metric. The protocol can therefore withhold GenAI–GPA claims when absolute gates fail, and a six-step laptop workflow is specified so educational researchers can apply the same checks without reproducing the full validation schedule. Full article

Through-going structures are widely distributed in rock masses of underground engineering, and their dip angles act as the core factor affecting the stress field and mechanical response of surrounding rock. To reveal the mechanical mechanism of rock masses containing through-going structures with different dip angles, this study adopts a combined method of theoretical derivation, indoor model testing and numerical simulation. Firstly, a plane strain mechanical model is established to classify Tectonically-induced Stress, Residual Gravitational Stress and engineering-induced stress, and the theoretical formulas for stress components, stress residual coefficient and stress deflection angle are derived. Secondly, rock-like specimens with through-going structures of various dip angles are prepared and biaxial compression tests are carried out to monitor mechanical parameters such as surrounding rock strain and peak strength. Finally, a large-scale numerical model is built by FLAC2D (version 7.0) software to simulate the whole process of stress equilibrium and excavation unloading of rock mass under a normal stress of 20 MPa. Then the data of principal stress, stress components, stress residual coefficient and deflection angle under different dip angles are extracted. The results show that the dip angle of through-going structure exerts a prominent regulatory effect on the rock mass stress field. With the increase of the dip angle, the Tectonically-induced Stress decreases continuously while the Residual Gravitational Stress rises gradually. The variation trend of stress deflection angle is highly consistent with structural dip angle, and the influence of Residual Gravitational Stress on deflection angle is limited. Due to the differences in loading modes and model sizes between indoor tests and numerical simulations, the evolution laws of stress residual coefficient show opposite trends, but both results verify the dominant effect of structural dip angle. Combined with theoretical, experimental and numerical results, the proposed theoretical system can effectively describe the stress evolution law of rock masses with through-going structures, which provides theoretical reference and technical support for the stability analysis of surrounding rock in similar underground engineering. Full article

The development of titanium-based surface alloys and coatings that combine extreme hardness with sufficient toughness remains a major challenge for components operating under severe friction and wear conditions. In this work, titanium–boride composite coatings were synthesized on commercially pure titanium by scanning electron beam surface alloying of preplaced boron. The influence of beam power (900, 1200, and 1500 W) on phase formation, microstructural evolution, and mechanical performance was systematically investigated. At 900 W, insufficient melting resulted in chemically and structurally heterogeneous coatings containing unreacted boron. Increasing the beam power to 1200 W promoted the formation of TiB and TiB₂ phases, leading to a maximum microhardness of approximately 5500 HV_0.2. At 1500 W, complete boron incorporation produced a graded architecture consisting of a Ti/TiB surface layer and a TiB₂-rich sublayer. This hierarchical microstructure exhibited a favorable combination of high hardness and the lowest coefficient of friction (0.21), representing a reduction of more than 50% compared with the untreated titanium substrate. These findings establish a clear relationship between electron beam processing conditions, microstructural development, and mechanical performance, demonstrating that scanning electron beam surface alloying is an effective strategy for tailoring high-performance Ti–B composite surfaces. The developed coatings show strong potential for aerospace and other advanced engineering applications requiring lightweight materials with high hardness and low friction. Full article

Probabilistic slope stability analysis requires tools that are both computationally efficient and accurate for uncertainty propagation. This study develops a reliability-oriented surrogate framework coupling a multilayer perceptron artificial neural network with particle swarm optimization (ANN–MLP–PSO). The model was trained on 2014 homogeneous slope cases drawn from literature records and mechanics-based simulations. PSO identified a best-performing six-hidden-layer architecture achieving a coefficient of determination of R² = 0.95 on the held-out test set. The trained surrogate was embedded in a probabilistic sampling framework to estimate the probability of failure (P_f), reliability index (β), and factor-of-safety quantiles, then applied to the Mansourieh slope near Beirut, Lebanon, under dry and wet conditions. Outputs were benchmarked against the First-Order Second-Moment method (FOSM), the Point Estimate Method (PEM), and Monte Carlo simulation (MCS). The comparison showed that the ANN–MLP–PSO surrogate reproduced the dry-to-wet changes in factor-of-safety distributions, probability of failure, and reliability index obtained from the conventional reliability methods under the same probabilistic assumptions, with wet-scenario failure probabilities ranging from approximately 86% to 99%. Despite quantitative differences, all four methods identified the same reliability trend and engineering interpretation. Once trained, the surrogate enabled rapid probabilistic evaluation without repeated deterministic calculations, providing an efficient tool for slope stability screening and uncertainty-aware geotechnical decision support. Full article