Search Results (414)

Zr alloy-shaped charge liners (SCLs) offer broad application prospects due to their multiple post-penetration damage effects. However, research on these liners is still in its early stages. The mechanisms of jet formation and penetration for Zr alloys SCL remain unclear, and the specific contribution of different liner regions to the penetration process is not yet understood. This gap in knowledge has limited their structural design to a black-box correlation between global structural parameters and macroscopic penetration efficiency. To address this gap, a region-tracing Smoothed Particle Hydrodynamics (SPH) simulation was employed. Following a strategy of “wall thickness layering + axial segmentation,” the Zr alloy liner was partitioned into ten characteristic regions. This methodology facilitated the tracking of material transport from each region during jet formation and penetration into an AISI 1045 steel target. The contribution of each region to the penetration depth was then quantitatively assessed via post-processing. For the first time, the “critical region” contributing most to penetration depth was identified, and the influence of the liner’s cone angle and wall thickness on the contribution of each region was revealed. This study enhances the theoretical framework for understanding the damage effects of Zr alloy shaped charge liners. It not only advances the fundamental understanding of jet penetration mechanisms but also provides a theoretical basis for the refined design and performance optimization of these liners. Full article

Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement amount. This study introduces a hybrid computational process that integrates metaheuristic optimization with symbolic regression to generate direct analytical equations for the estimation of the required CFRP area. First, a comprehensive database containing 1300 optimal strengthening scenarios was generated using the Jaya optimization algorithm under the constraints specified in ACI 440.7R and ACI 530. The resulting dataset was subsequently processed through symbolic regression using the PySR platform to identify explicit mathematical relationships between structural parameters and the optimum CFRP area. Most traditional machine learning approaches operate as black-box predictors. In contrast, the proposed approach generates interpretable closed-form expressions that can be used directly in engineering calculations. Two models were derived from the Pareto-optimal solution set. The first model is a simplified equation emphasizing algebraic simplicity. The second model prioritizes prediction accuracy. The simplified formulation achieved a coefficient of determination of approximately 0.992. The accuracy-focused model achieved a value above 0.997 with very low prediction errors. Validation studies with independent test samples showed that the obtained equations are reliable. The average error for the simplified model is below 4%, and for the high-accuracy model, it is approximately 2%. The results demonstrate that combining the optimization-generated datasets with symbolic regression makes it possible to obtain transparent design equations. These equations eliminate iterative design processes and provide a fast and reliable estimation tool for CFRP strengthening of masonry walls. Full article

Accurate shape control is paramount for ensuring the quality of hot-rolled strip products, which is significantly challenged by the high dimensionality, inherent nonlinearity, and strong coupling of process parameters. While machine learning (ML) methods have demonstrated superior predictive performance in product quality modeling, the inherent “black-box” nature and lack of transparency severely undermine system reliability and hinder practical deployment. Existing explainable artificial intelligence (XAI) approaches predominantly rely on statistical correlations while overlooking the underlying causal mechanisms among coupled variables, which severely limits the validity of explanations. To address these limitations, a causal XAI diagnosis and optimization framework for hot-rolled strip shape is proposed. Initially, a hybrid causal structure learning module is established, which integrates domain knowledge with the NOTEARS-MLP algorithm to accurately reconstruct the causal topology and decode the complex coupling mechanisms among process parameters. Subsequently, a high-performance quality prediction module utilizing AutoML techniques is constructed to establish a robust predictive baseline. Furthermore, a causal XAI and quality optimization module is introduced, which incorporates causal constraints into standard Shapley additive explanation (SHAP) analysis for transparent diagnosis, and employs piecewise linear analysis (PLR) to generate sample-specific optimization strategies. Comprehensive experimental validation demonstrates that the prediction module significantly outperforms state-of-the-art ML approaches across multiple performance metrics. Additionally, comparative analysis reveals that the optimization strategy based on causal feature attribution exhibits 14.7% defect rate reduction over the associational baseline, which is effective, efficient and establishes a new benchmark for causal explainability in industrial process optimization applications. Full article

Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an improved water cloud model (IWCM) with machine learning algorithms. Multi-modal unmanned aerial vehicle (UAV) experiments were conducted during the heading stage of winter wheat over two consecutive years (2024–2025) using a synchronized system equipped with a miniature synthetic aperture radar (MiniSAR) and a multi-spectral sensor. The core innovation of the proposed framework lies in the IWCM, which explicitly decouples vegetation and soil scattering contributions by incorporating fractional vegetation cover, thereby deriving physically meaningful soil backscatter coefficients from complex microwave signals. Unlike traditional methods that treat remote sensing variables as black box inputs, our approach employed these physics-derived features to guide data-driven modeling. Four feature input schemes including spectral reflectance, vegetation indices, MiniSAR polarimetric parameters, and their multi-source fusion were systematically evaluated using back propagation neural network (BPNN) and random forest (RF) regressors. The results demonstrated that the proposed framework significantly enhances retrieval performance. Notably, the RF model driven by spectral band reflectance within this physically constrained architecture achieved optimal accuracy, with a coefficient of determination (R²) of 0.865, a mean absolute error (MAE) of 0.0152, and a root mean square error (RMSE) of 0.0197. Compared to purely empirical approaches, the IWCM significantly improved the physical interpretability of microwave polarimetric characteristics, enabling the multi-source data fusion to better represent the interactions among vegetation, soil, and microwave scattering. This study demonstrated that integrating mechanistic models with multi-source UAV remote sensing data not only improves soil water content retrieval accuracy in winter wheat fields but also provides a valuable reference for developing operationally applicable and physically interpretable farmland soil water content monitoring systems. Full article

Recent studies have shown that deep neural networks can be misled by adversarial examples that involve only imperceptible perturbations. Among these, one-pixel attacks (OPA) represent an extreme yet powerful threat, as they alter only a single pixel of an input image while causing misclassification. While prior work has demonstrated the effectiveness of OPAs on low-resolution datasets, extending these attacks to high-resolution images poses a significant challenge due to the dramatic increase in the number of pixels and the resulting expansion of the search space. In this paper, we address this challenge by proposing a scalable one-pixel attack framework for deep neural networks on high-resolution images. The key difficulty in high-resolution OPAs lies in identifying a vulnerable pixel among tens of thousands of candidates under a black-box setting, where exhaustive pixel-wise probing is prohibitively expensive. To overcome this limitation, we decompose the attack into two phases. In the first phase, we efficiently identify a small set of promising pixel locations using a hierarchical patch-based search strategy, which iteratively prunes large image regions via coarse-grained patch perturbations, thereby substantially reducing the number of required model queries. In the second phase, for each selected pixel candidate, we search for adversarial RGB values using a black-box optimization method based on momentum-accelerated finite-difference gradient estimation. We evaluate our method on popular deep neural network architectures using high-resolution ImageNet images. The experimental results demonstrate that our approach achieves high attack success rates while significantly reducing query cost and improving the quality of the resulting adversarial perturbations compared to existing strategies. Full article

Investigating the influence of landscape evolution on river eutrophication is critical for optimizing spatial patterns to improve water quality. Machine learning (ML) models can capture the complex relationship between landscape metrics and water quality, but their black-box property restricts the interpretability of the underlying mechanisms and makes it difficult to forecast future trends in water quality. To address this, we developed a novel framework that, for the first time, couples an interpretable ML model with the Patch-generating Land Use Simulation (PLUS) model for eutrophication index (EI) prediction. This approach elucidates the response of river eutrophication to landscape dynamics and forecasts future river EI trends. The random forest regression (RFR) model outperformed other algorithms in quantifying these relationships (R² = 0.934 for training, 0.711 for testing). SHAP analysis revealed that landscape metrics contributed 81.78% to the river EI, far exceeding climate factors (18.22%). Consequently, landscape evolution emerged as the dominant explanatory factor. Scenario simulations indicated that while the ecological protection (EP) scenario effectively mitigates river eutrophication, the urban development (UD) scenario significantly exacerbates it. Specifically, under the UD scenario, the average EI in urban sub-watersheds is projected to reach 60.78 by 2040, approaching heavy eutrophic levels. Our findings inform spatial optimization strategies for river eutrophication management and facilitate the design of targeted, localized water ecological protection policies in subtropical monsoonal basins. Full article

The high energy intensity of public buildings, especially those with HVAC systems, calls for advanced control strategies such as Model Predictive Control (MPC) to balance energy efficiency and thermal comfort. However, the performance of MPC relies critically on the accuracy and robustness of building cooling and heating load calculations, which remain challenging, particularly for buildings with complex dynamic characteristics. This study proposes a simplified modeling-based MPC approach and investigates the influence of three different load calculation methods on controller performance: a physics-driven white-box model, a data-driven black-box model, and a novel Closed-Loop Load Grey Model (CLLGM). Under identical outdoor conditions during summer cooling operation, the three controllers exhibit distinct performance disparities: although the proposed CLLGM-based controller only reduces the load prediction MAPE by 0.63% compared with the black-box model, it improves the temperature control stability index (TDI) by 80.43% and increases the comprehensive score from the MPC multi-objective optimization function by 16.55%. Its key advantage is that it can use on-site temperature measurements as feedback to correct the cooling load, making it better suited for simulation and computation in MPC. Full article

Heterogeneous multi-core systems (HMCSs) typically face a dilemma: heuristics (e.g., Linux CFS) are fast but blind to global constraints, while meta-heuristics (e.g., GAs) are globally optimal but too slow for real-time OS interaction. To bridge this gap without relying on “black-box” neural networks, we introduce the Tissue P System-Inspired Task Allocator (TPSTA). By mapping HMCS and parallel task scheduling to Tissue P System models and vectorized linear algebra problems, TPSTA achieves a computational complexity of $\mathrm{O}\left(\mathrm{M}/\mathrm{W}\right)$, effectively compressing the decision space. Our rigorous evaluation across four dimensions reveals a system strictly bound by physical constraints rather than algorithmic heuristics. (1) Under sufficient resource provisioning (four chips), TPSTA achieves a 0.00% Deadline Miss Ratio (DMR). Crucially, stress tests on constrained hardware (two chips) show graceful degradation to a 12.88% DMR, matching the optimal theoretical bound of EDF, whereas standard heuristics collapse to failure rates > 68%. On a massive 4096-core cluster, TPSTA outperforms the Linux GTS scalar baseline by 14.4×, maintaining low latency where traditional algorithms fail (>8 s). (3) Adaptability: The system demonstrates adaptive routing in handling hardware heterogeneity; without explicit rule-coding, it autonomously prioritizes data locality during NUMA transfers and migrates compute-bound tasks during thermal throttling events. (4) Physical Limits: Finally, our roofline analysis confirms that while the algorithmic speedup is theoretically linear, practical performance saturates at ~375× due to the Memory Wall, validating the isomorphism between synaptic bandwidth and hardware memory channels. Full article

Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs) is justified by performance gains over established feature engineering paradigms (e.g., XGBoost) under the constraints of sparse altimetry supervision. We established a rigorous comparative framework across four mainstream products—ALOS World 3D, Copernicus DEM, SRTM GL1, and TanDEM-X—using Sichuan Province, China, as a representative natural laboratory. Our results reveal a fundamental scale mismatch (where the ~485 m average spacing of sampled altimetry footprints dwarfs the local terrain resolution): despite their topological complexity, Hybrid GNN models fail to establish a statistically significant accuracy advantage over the systematically optimized XGBoost baseline, demonstrating RMSE parity. Mechanistically, we uncover a critical divergence in decision logic: XGBoost relies on a stable “Physics Skeleton” consistently dominated by deterministic features (terrain aspect and vegetation density), whereas GNNs exhibit severe “Attribution Stochasticity” ($\rho \approx$ 0.63–0.77). The GNN component acts as a residual-dependent latent feature learner rather than discovering universal topological laws. We conclude that for geospatial regression tasks relying on sparse supervision, “Physics Trumps Geometry.” A “Feature-First” paradigm that prioritizes robust, domain-knowledge-based physical descriptors outweighs the indeterminate complexity of “Black Box” architectures. This study underscores the imperative of prioritizing explanatory stability over marginal accuracy gains to foster trusted Geo-AI. Full article

Accurate performance prediction of variable-speed centrifugal chillers is important for building energy optimization and the development of digital twins in HVAC systems. In practice, obtaining extensive operational data is costly, creating a prevalent “small-sample” dilemma under which conventional data-driven models are prone to overfitting with poor extrapolation capability. While recent Physics-Informed Neural Networks (PINNs) incorporate system-level thermodynamic constraints (e.g., COP definitions), they typically treat the centrifugal compressor as a thermodynamic black box, neglecting its inherent fluid dynamic characteristics; consequently, extrapolated predictions may be physically inconsistent or fall into unsafe operating regions such as compressor surge. To address this gap, this paper proposes an Aero-thermodynamic Physics-Informed Neural Network (Aero-PINN) that introduces three mechanisms into the PINN loss function: (1) dimensionless aerodynamic similarity mapping governed by affinity laws, (2) a surge boundary constraint that prevents non-physical extrapolations, and (3) an aerodynamic–electrical energy coupling validation. Experimental validation on 420 real-world variable-speed test records shows that the Aero-PINN achieves a COP RMSE of 0.04 and a COP MAPE of 0.3%, outperforming standard MLP and polynomial baselines. Moreover, 100% of the extrapolated operating points satisfy all fluid dynamic safety and energy efficiency constraints. This framework provides a reliable, physics-constrained small-sample learning approach, facilitating factory calibration and reduced-test digital modeling for chiller plants. Full article

As product aesthetics increasingly drive consumer preference, quantitative evaluation remains hindered by subjective evaluation biases and the black-box nature of modern artificial intelligence. This study proposes an advanced machine learning framework incorporating sensitivity-aware morphological features for the aesthetic evaluation of industrial products, with automotive design as a representative case. An aesthetic index system and its quantitative formulations are first developed to capture the morphological characteristics of product form. Subjective weights are determined via grey relational analysis (GRA), while objective weights are calculated using the coefficient of variation method (CVM) integrated with the technique for order preference by similarity to an ideal solution (TOPSIS). A game-theoretic weighting approach is then employed to fuse subjective and objective weights, thereby establishing a multi-scale aesthetic evaluation system. Sensitivity analysis is applied to identify six key indicators, forming a high-quality dataset. To enhance prediction performance, a novel model—improved lung performance-based optimization with backpropagation neural network (ILPOBP)—is proposed, where the optimization process leverages a maximin latin hypercube design (MLHD) to enhance exploration efficiency. The ILPOBP model effectively predicts aesthetic ratings based on limited morphological input data. Experimental results demonstrate that the ILPOBP model outperforms baseline models in terms of accuracy and robustness when handling complex aesthetic information, achieving a significantly lower test set mean absolute relative error (MARE = 4.106%). To further enhance model interpretability, Shapley additive explanations (SHAP) are employed to elucidate the internal decision-making mechanisms, offering reverse design insights for product optimization. The proposed framework offers a novel and effective approach for integrating machine learning into the aesthetic assessment of industrial product design. Full article

This study addresses the gap between predictive optimization and monitoring-oriented risk concentration by introducing a hierarchical Fuzzy Cognitive Map (FCM) framework for financial risk assessment. Financial distress prediction models are employed to estimate firm-level default probabilities and are required to comply with regulatory standards. IFRS 9 and Basel III/IV frameworks emphasize model explainability, scenario analysis and causal transparency, which are essential for compliance purposes. The methodology aggregates correlated financial ratios into financial concepts through unsupervised clustering. Concepts interact through a learned coupling matrix and a controlled multi-step propagation, which enables the amplification of risk signals. A small residual correction is applied at the final readout, preserving the interpretability of the proposed framework. The framework was applied to two severely imbalanced benchmark bankruptcy datasets. It achieved higher precision–recall performance than Logistic Regression (PR–AUC $\approx 0.32$ vs. $0.27$), improved calibration (Brier score $\approx 0.046$ vs. $0.089$) and maintained competitive Recall@Top–K under tight supervisory monitoring budgets. Hierarchical FCM achieved predictive performance comparable to nonlinear models while maintaining concept-level interpretability. Our findings demonstrate that structured concept aggregation combined with interaction-based propagation provides a transparent alternative to purely predictive black-box models in financial distress assessment and is aligned with regulatory frameworks. Full article

In the context of the cross-disciplinary integration of data science and archival management, archival openness auditing stands as a critical process for public information access but faces challenges in processing long texts with sparse core information. To address this, this paper proposes an Assisted Archival Auditing Model (ALC-MCFN) based on deep semantic understanding and decision transparency. The model aims to leverage intelligent analytics to optimize the decision-making process of archival openness. Regarding deep semantic understanding, a semantic-aware dynamic truncation mechanism is first employed to effectively remove redundancy while preserving key logical structures. Subsequently, by fusing global, local, and logical semantic features extracted by BERT, TextCNN, and TextGCN, the model overcomes the limitations of single-view feature representation. Furthermore, to address the “black box” issue of deep learning in compliance auditing, the SHAP method is introduced to provide post hoc interpretability. By visualizing the contribution of key textual features to the auditing results, the model enhances the transparency and trustworthiness of decision-making. Experimental results demonstrate that ALC-MCFN outperforms mainstream baseline models, with a 77.21% F1-score on the self-built archival domain OParchives dataset (1.15 percentage points higher than the BERT baseline), providing robust data science support for risk control and efficiency improvement in intelligent archival management. Full article

Recently, the adaptive regularized proximal quasi-Newton (ARPQN) method has demonstrated a strong performance in solving composite optimization problems over the Stiefel manifold. However, its reliance on first-order information limits its applicability to scenarios where gradient and Hessian evaluations are unavailable or costly. In this paper, we propose a zeroth-order adaptive regularized proximal quasi-Newton method (ZO-ARPQN) for black-box composite optimization over Riemannian manifolds, particularly the Stiefel and symmetric positive definite (SPD) manifolds. The proposed method estimates the Riemannian gradient and curvature information through randomized one-point finite-difference approximations and adaptively updates a regularized quasi-Newton matrix to capture the local manifold geometry. Theoretically, we established global convergence and complex analyses under mild assumptions. More importantly, by incorporating curvature-aware regularization and random perturbations in the proximal quasi-Newton framework, we proved that ZO-ARPQN can escape strict saddle points with a high probability. This guarantees convergence to a stationary point, even in the absence of explicit gradients. Extensive numerical experiments were conducted on manifold-constrained problems, including sparse PCA and robot stiffness tuning. These demonstrated that ZO-ARPQN shows a competitive convergence behavior compared with other state-of-the-art Riemannian optimization methods, while requiring only function evaluations. Full article

The formation of longitudinal queues is critical for biological and artificial swarm systems to achieve efficient long-distance navigation. However, the “black-box” nature of conventional deep reinforcement learning models often obscures the microscopic rules driving the emergence of such ordered behaviors. To address this challenge, this paper proposes an interpretable computational model of collective behavior based on Projective Simulation and Episodic Compositional Memory, which enables individuals to learn decision-making strategies within a transparent state–action network. Simulation results demonstrate that the swarm can self-organize into stable and highly elongated longitudinal queues. Crucially, through visualization of microscopic strategies, we reveal a deterministic target-priority mechanism: when local neighbor alignment conflicts with global target orientation, individuals learn to strictly prioritize the target direction, serving as the key driving force for queue formation. Further parametric analysis indicates that the action space granularity exerts a nonlinear impact on stability, identifying moderate control precision as the optimal choice. This study not only provides a transparent computational explanation for the self-organization mechanism of queues in collective motion but also offers a theoretical foundation for designing interpretable swarm navigation systems. Full article