Search Results (5,244)

Three-dimensional geological structural modelling provides the geometric framework for sub-surface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. In particular, the conventional surface-based workflow follows a sequential pipeline—from seismic interpretation through manual intersection editing to surface generation and pillar gridding—in which geological knowledge is embedded only implicitly through operator-dependent parameter tuning, making knowledge transfer and model reproducibility difficult. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a three-tier knowledge architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a knowledge-driven intersection line generation algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust-nappe and deep complex structural styles. Compared with a conventional surface-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks. These results demonstrate that explicit integration of formalised geological knowledge into the modelling pipeline can substantially enhance both accuracy and efficiency across a range of structural settings. Full article

This paper investigates the temporal evolution of high-speed compressible fluids governed by the two-dimensional Euler equations in irregular flow fields using the Fourier Neural Operator (FNO). We reconstruct the irregular flow field point set into sequential format compatible with FNO input requirements, and then embed temporal bundling technique within a recurrent neural network (RNN) for multi-step prediction. We further employ a composite loss function to balance errors across different physical quantities. Experiments are conducted on three different types of irregular flow fields, including orthogonal and non-orthogonal grid configurations. Then we comprehensively analyze the physical component loss curves, flow field visualizations, and physical profiles. On non-orthogonal grids, our method consistently achieves improvements in both computational efficiency and error compared to other baseline models. Results demonstrate that our approach achieves high accuracy, as evidenced by maximum relative $L_2$ errors of $(0.75\%,0.56\%,0.35\%)$ for ($p,T,$$\parallel \mathbf{u}\parallel$) respectively (where p, T, and $\parallel \mathbf{u}\parallel$ denote pressure, temperature, and velocity magnitude), and offers substantial improvements in computational efficiency over traditional numerical methods. Within this data-driven context, the method accurately and efficiently simulates the temporal evolution of high-speed compressible flows in irregular domains. Full article

High-sensitivity detection of direct current (DC) partial discharge (PD) in HVDC gas-insulated equipment (GIE) remains challenging because conventional electrical measurements are susceptible to ambient interference and DC PD lacks a phase reference for phase-resolved analysis. Although photon counting techniques provide exceptional sensitivity and noise immunity, their diagnostic application has so far been confined to alternating current (AC) conditions. In this study, a photon-counting-based measurement platform was developed to investigate DC PD generated by three representative gas–solid insulation defects, namely conductor protrusion, surface-attached metal, and free metallic particle. Photon pulse sequences were acquired under both positive and negative voltage polarities. Successive inter-pulse time intervals were then mapped into two-dimensional kernel density estimation heatmaps to visualize defect-dependent temporal characteristics. A Random Forest classifier, integrated with SHapley Additive exPlanations (SHAP) for feature reduction, was employed for quantitative classification. The proposed method achieved classification accuracies of 97.50% and 99.17% for positive and negative polarities, respectively. Notably, the model adaptively prioritized angular-distribution features over radial-distribution features under space-charge-suppressed conditions. These results demonstrate the feasibility of photon-counting-based time-domain characterization and defect classification for DC PD, providing a quantitative, less experience-dependent framework for insulation defect identification in DC gas-insulated systems. Full article

Behavioral authentication (BA) systems verify user identity claims based on unique behavioral characteristics using machine learning (ML)-based classifiers trained on user behavioral profiles. Although effective, ML-based BA systems face serious privacy threats, including profile inference and reconstruction attacks. This paper presents RUIP-BA (Renewable, Unlinkable, and Irreversible Privacy-Preserving Behavioral Authentication), a non-cryptographic framework designed for settings where computational resources may be limited. Random Projection (RP) maps behavioral profiles into lower-dimensional protected templates while approximately preserving utility-relevant geometry, and local Differential Privacy (DP) injects calibrated stochastic perturbations to provide formal privacy protection. The proposed design jointly targets the ISO/IEC 24745 requirements of renewability, unlinkability, and irreversibility. We provide complete algorithmic realizations for enrollment, verification, template renewal, unlinkability testing, and GAN-based adversarial privacy evaluation. We also introduce rigorous formal privacy derivations and proofs under explicit assumptions, including formal security games, information-theoretic theorem-level guarantees, Cramér–Rao lower bounds for irreversibility, full Jensen–Shannon divergence derivations for unlinkability, and a GAN Nash-equilibrium attack bound. Comprehensive dimensionality ablation across all three modalities confirms robust utility at compact template sizes, and an expanded analysis of the privacy–utility trade-off under varying $ϵ$ values is provided. Experiments on voice, swipe, and drawing datasets show authentication accuracy above $96\%$ while sharply limiting feature recoverability under strong GAN-based attacks. All reported FAR/FRR figures are single-session best-case estimates; cross-session longitudinal evaluation remains future work. RUIP-BA provides a scalable, mathematically grounded, and deployment-ready privacy-preserving BA solution. Full article

316LN stainless steel is widely used in critical nuclear fusion structural components due to its excellent mechanical properties and machinability. However, its high thermal expansion coefficient and low thermal conductivity promote welding distortion, while work hardening causes residual stress accumulation. Thermo-elastic–plastic finite element modeling (FEM) is the primary numerical method for predicting these effects. Yet, despite hardware advances, full-scale simulations—especially for thick plates with multi-pass welds—remain computationally expensive, hindering the balance between efficiency and accuracy. To address the inherent trade-off between welding efficiency and dimensional accuracy in multi-pass, multi-layer welding of thick-section components, this study employs MSC. Marc to develop a finite element model of a 15 mm thick butt-welded joint fabricated from 316LN stainless steel. Three distinct heat source models—instantaneous, enhanced moving, and moving element-set—are systematically implemented to simulate transient temperature fields, residual stress distributions, and welding deformation. All numerical predictions are rigorously validated against experimental measurements to comprehensively assess both accuracy and computational efficiency. Results indicate that: (i) the predicted molten pool geometries and characteristic thermal cycle profiles from all three models exhibit strong agreement with experimental observations; (ii) longitudinal residual stress distributions predicted by all models align closely with measured values; (iii) transverse residual stresses predicted by the moving element-set and enhanced moving heat sources agree well with experiments, whereas those from the instantaneous heat source show marked deviation; (iv) angular distortion predictions from the moving element-set heat source achieve over 90% conformity with experimental data, while the instantaneous heat source substantially underestimates angular distortion, and the enhanced moving heat source yields approximately 65% agreement; and (v) in terms of computational efficiency, the instantaneous heat source requires only ~40% of the computation time needed by the moving heat source. Full article

Accurate Structural Health Monitoring of offshore wind turbines is critical for ensuring their long-term operational safety in harsh marine environments. Although displacement is a fundamental metric for assessing structural deformation and stress distribution, its direct measurement in open-ocean conditions is severely hindered by environmental interference and the absence of stable spatial references. Consequently, reconstructing displacement from structural acceleration through double integration is widely adopted, yet it suffers from severe baseline drift. Furthermore, existing drift-mitigation techniques often rely on empirical parameter selection and are limited to single-point reconstructions, failing to capture the full three-dimensional (3D) spatial attitude of the structure. To address these limitations, this paper proposes a novel 3D spatial attitude reconstruction framework based on advanced drift removal and spatial interpolation. First, an improved drift removal algorithm is developed to accurately eliminate baseline errors from acceleration signals, ensuring the physical fidelity of the reconstructed local displacements. Subsequently, cubic spline interpolation is utilized to extrapolate these discrete local measurements into a comprehensive full-field attitude profile of the entire turbine structure. The performance and robustness of the proposed method are systematically verified through numerical simulations and finite element analysis. Finally, its engineering applicability and accuracy are further validated via laboratory experiments and field measurements. The proposed framework effectively mitigates noise sensitivity and significantly enhances the accuracy of full-field attitude reconstruction, providing a reliable foundation for refined structural health assessments of OWTs. Full article

The rapid advancement of unmanned aerial vehicles (UAVs) has greatly increased the application of various swarm intelligence algorithms in UAV path planning. To address the potential issues with the dung beetle optimizer (DBO) in UAV trajectory planning, such as low convergence accuracy, tendency to get trapped in local optima, and imbalance between global search and local exploration, a hybrid algorithm termed DBO-PSO is proposed by integrating DBO with particle swarm optimization (PSO) to solve the UAV path planning model. The Kent chaotic map is introduced to enhance population diversity and distribution uniformity, and the velocity–position update mechanism of PSO is incorporated into DBO to strengthen its global search capability. Comparative experiments are conducted on CEC2022 benchmark functions, and multiple classical swarm intelligence algorithms are selected for comparison using six evaluation metrics, along with Wilcoxon rank-sum and Friedman statistical tests. An ablation study is also performed to evaluate the contribution of each improvement component. The path planning experimental results demonstrate that compared to DBO, PSO, IDBO, and ECFDBO under the population size of 50, DBO-PSO reduces the total path cost by 44.2%, 17.3%, 8.9%, and 45.1%, respectively. The ablation study verifies that both improvement components contribute positively, which demonstrates its competitive performance and practical applicability in UAV three-dimensional path planning. The source codes to support the presented results are publicly available on GitHub. Full article

Transfer accuracy is widely used to evaluate orthodontic indirect bonding workflows, particularly in the context of digital CAD/CAM planning and three-dimensional bracket positioning. However, substantial heterogeneity in its definition, measurement, and reporting may limit comparability and clinical interpretability across systematic reviews. This methodological umbrella review examined how transfer accuracy is operationalized as an outcome construct, with specific focus on conceptual definitions, dimensional frameworks, reference systems, measurement pipelines, and interpretative strategies rather than pooled quantitative deviation estimates. A systematic search of major biomedical databases was conducted to identify systematic reviews evaluating transfer accuracy in orthodontic indirect bonding. Data extraction was performed independently by two reviewers using a predefined methodological mapping framework, and methodological quality was assessed with AMSTAR-2. Four systematic reviews met the inclusion criteria. Across reviews, transfer accuracy was operationalized through heterogeneous linear and angular geometric deviation metrics derived from planned–achieved bracket position comparisons, without use of a standardized composite accuracy indicator. Nevertheless, substantial heterogeneity was found in outcome definitions, dimensional architectures, reference system selection, and analytical workflows, resulting in structurally non-equivalent representations of transfer accuracy and limiting cross-review comparability. Within the included systematic reviews, transfer accuracy functioned primarily as a workflow-dependent geometric measurement construct rather than as an outcome systematically operationalized within clinically validated frameworks. We recommend standardized construct definitions, mandatory reporting of reference systems and registration algorithms, routine uncertainty quantification, and harmonized dimensional frameworks as essential steps toward valid evidence synthesis, reproducible digital orthodontic workflows, and clinically interpretable transfer accuracy measurement. Full article

To address the defects of the traditional egret swarm optimization algorithm (ESOA) in high-dimensional complex optimization problems, such as low optimization accuracy, weak ability to escape from local extrema, rapid decay of population diversity, and insufficient efficiency in the late convergence stage, an improved egret swarm optimization algorithm (IESOA) combining variable-factor weighted learning and adjacent generation dimension crossover strategy is proposed. Firstly, a dynamic change rule of core model parameters (exploration factor ω and exploitation factor μ) is constructed to adaptively adjust with the iteration process, so as to balance global exploration and local exploitation capabilities. Secondly, a multi-individual variable-factor weighted learning mechanism is designed to enable offspring individuals to inherit the position information of following individuals, sub-population optimal individuals, and global optimal individuals simultaneously, avoiding excessively fast assimilation of the population. Furthermore, an adjacent generation dimension crossover strategy is established to update the optimal individual based on the priority principle of absolute dimension difference, fully retaining the historical optimal dimension information. Finally, a preferred mutation reverse learning strategy is integrated to further enhance the local extremum escape ability and convergence accuracy of the algorithm. The IESOA is compared with eight algorithms, including PSO, DE, SBOA, BKA, HHO, DOA, and the original ESOA on CEC2014 and CEC2019 benchmark test suites. The results show that IESOA presents significant advantages in optimization accuracy, convergence speed, and stability. The algorithm is applied to three typical engineering optimization problems: reinforced concrete beam design, welded beam design, and pressure vessel design, which effectively reduces the structural design cost and verifies its application value in practical engineering. Full article

Predictive analytics has become increasingly important in educational decision-making, supporting at-risk identification and adaptive tutoring. The accurate early prediction of school achievement can enable timely intervention. Using the Math Students dataset, which contains data on students from two Portuguese secondary schools, we model three categorical outcomes derived from the students’ final grade, namely the final grade level (low, medium, high), its qualitative evaluation (fail, satisfactory, good, excellent), and the final pass/fail outcome. After preprocessing, three filter methods—Correlation-Based Feature Subset Selection (CFS), Correlation Attribute Evaluation (CorrEval), and Information Gain (InfoGain)—are applied to reduce the dimensionality of the datasets. Nine classifiers (Naive Bayes, Logistic, MLP, SMO, IBk, Bagging, J48, Random Forest, Random Tree) are evaluated using ten-fold cross-validation in the Waikato Environment for Knowledge Analysis (Weka) platform. Random Forest with InfoGain achieves 90.7% accuracy on the three-band task, while Bagging with InfoGain achieves 92.5% on the binary pass/fail outcome, outperforming benchmarks in prior Educational Data Mining (EDM) studies. Results confirm that prior academic performance indicators (first- and second-period grades) and failure history dominate predictive power and contribute substantially to the success of ensemble models, particularly when paired with feature selection methods that reduce noise and highlight relevant attributes. Full article

A novel robot behavior generation method combining imitation learning with diffusion models elegantly addresses multi-modal action distributions, adapts to high-dimensional action spaces, and demonstrates impressive training stability. It significantly improves success rates across nine diverse tasks on three different robot simulation benchmarks, but comes with longer training times and slower inference speed. This paper proposes a novel architecture, DiT1dLnet, applied to DDPM for training and inference. DiT1dLnet improves accuracy across various robotic simulation tasks while accelerating training and inference speed by 50–100%. We benchmarked its performance on nine different tasks using three distinct robots. Full article

Surface stain contamination poses a critical barrier to the automated, high-precision fiber identification required for industrial-scale waste textile recycling. In this study, a dataset comprising 120 physical specimens (yielding 1200 regions of interest, ROIs) across 12 contamination categories was constructed by contaminating cotton, polyester, and poly-cotton blend textiles with carbon black, protein, and oil stains. The spectral interference effects of stains—including baseline drift and spectral overlapping induced by physical shielding and chemical absorption—were systematically analyzed. To identify the optimal classification pipeline, three mathematical preprocessing methods (First Derivative, FD; Standard Normal Variate, SNV; and Multiplicative Scatter Correction, MSC) were evaluated alongside Support Vector Machine (SVM) and One-Dimensional Convolutional Neural Network (1D-CNN) models. Results show that among the SVM-based pipelines, the FD-SVM model effectively resolves overlapping absorption peaks, achieved an average accuracy of 98.17% ± 1.33%, but remains highly dependent on mathematical preprocessing. In contrast, the 1D-CNN model employing a progressive stacking architecture of multi-scale convolutional kernels attains a highly robust mean accuracy of 99.58% ± 0.56% under a strict specimen-level 10-fold cross-validation. It achieves this by directly utilizing radiometrically calibrated raw spectra, thereby effectively bypassing manual spectral feature engineering. These findings demonstrate that Hyperspectral Imaging coupled with end-to-end deep learning provides a feasible and industrially deployable solution for simultaneous stain detection and fiber identification in waste textile sorting. Full article

The new projective solutions methods (PSMs) for solving the Stokes, Oseen, and Brinkman flow problems are presented in this paper. They automatically satisfy the governing equations and are therefore Trefftz-type methods. Utilizing the third-order formulation and three-dimensional analytic functions, we derive a meshless Trefftz-type method to solve three-dimensional Stokes flow problems. The Oseen and Brinkman equations are transformed into four coupled third-order/first-order partial differential equations. The projective-type particular solution (PTPS) is obtained via a projective function in terms of the projective variable; the third-order ordinary differential equations (ODEs) with constant coefficients are derived to determine the projective functions. The Trefftz-type PSM is extremely accurate, because the governing equations (including the incompressibility condition) are implemented automatically. For the Brinkman equations, the general solutions of velocity and pressure are presented by using the Helmholtz function and a harmonic function, whose corresponding Trefftz-type numerical method is developed. Upon comparison with the method of fundamental solutions (MFS), the new methods exhibit some advantages, including lower condition numbers, faster convergence, and better accuracy. We also apply the Trefftz-type PSM to solve the exterior problem of the Stokes equations, where the velocity tends to zero at infinity. Full article

Accurate determination of morphometric body measurements is essential for monitoring growth, evaluating production traits, and supporting selection decisions in cattle breeding. However, traditional measurement methods require direct contact with animals, which may increase labor requirements, negatively affect animal welfare, and pose safety risks for operators. This study evaluated the relationship and agreement between traditional tape measurements and smartphone-camera-based morphometric measurements in cattle. A total of 100 cattle raised in the Mediterranean region of Türkiye, including 50 Holstein and 50 Simmental animals, were included in the study. Withers height, body length, rump height, and forechest width were measured using both conventional tools and a smartphone-camera-based method. Regression analyses demonstrated strong linear relationships between methods, particularly for body length and withers height (R² = 0.564–0.961). Bland–Altman analysis revealed small but significant systematic differences between methods, with camera-based measurements generally producing slightly higher values than tape measurements. The strongest agreement was observed for body length measurements, whereas wider limits of agreement were detected for anatomically complex traits, such as rump height and forechest width. Although the findings support the potential applicability of smartphone-based morphometric measurements as a practical and contactless alternative under field conditions, measurements were obtained only from a single lateral view, which should be considered an important methodological limitation. Future studies using multi-view or three-dimensional imaging systems may further improve measurement accuracy and agreement. Full article

Land subsidence around the gas exploitation area in Sidoarjo Regency, East Java Province, Indonesia, located in the northeastern part of Java Island, has been detected since 2006. This subsidence occurs not only in the vicinity of the Sidoarjo mud eruption but also extends to the Wunut and Tanggulangin areas, where several gas production wells are located. This study identifies land subsidence using integrated geodetic methods, including InSAR (PS-InSAR and SBAS), GNSS, and levelling observations. InSAR provides spatially continuous measurements from satellite radar imagery, while GNSS and levelling observations at control points are used to evaluate and interpret the detected deformation. GNSS provides point-based three-dimensional displacement, whereas levelling offers high-accuracy vertical displacement information. The results show notable differences between the InSAR approaches. PS-InSAR indicates a maximum subsidence of −249.4 mm, with a velocity of −41.01 mm/year, whereas SBAS yields a maximum subsidence of −510.43 mm and a velocity of −86.08 mm/year. GNSS observations indicate an average subsidence rate of −52.2 mm/year during 2020–2022, while levelling results show an average subsidence rate of −205.4 mm/year during 2022–2023. These differences are primarily attributed to variations in spatial sampling, temporal coverage, and the measurement characteristics of each method, particularly under rural and wetland conditions with limited persistent scatterers. Overall, the integration of InSAR, GNSS, and levelling data provides a more comprehensive interpretation of land subsidence and highlights the importance of considering method-dependent uncertainties when comparing deformation results from different geodetic techniques. Full article