Search Results (12,573)

Unmanned aerial vehicles (UAVs) are widely used for documentation, surveying, and 3D modeling in the built environment, yet their outputs often remain difficult to reuse for immersive comparison of alternative construction scenarios. This study presents a low-cost UAV-to-3DGS-to-VR workflow for constructing scenario-comparable immersive environments for built-environment review. The workflow combines multi-angle UAV imagery, point-cloud-based geometric anchoring, 3D Gaussian Splatting (3DGS), and Unity-based virtual reality (VR) to transform drone-captured reality into a reusable scene for controlled scenario comparison. The workflow is demonstrated in Middenbeemster, the central town of the Beemster polder World Heritage property. One present-condition scene (M0) and three alternative construction scenarios (M1 to M3) were created within a shared spatial reference. Reconstruction quality was assessed using PSNR and SSIM, and the VR scenes were further evaluated through eye-tracking, head-motion recording, and subjective ranking. The results indicate that the workflow can generate visually reliable and directly comparable immersive scenes from UAV data in this case study. Behavioral and subjective findings showed a consistent pattern, with M1 appearing more compatible than M2 and M3 in this pilot evaluation. The study contributes a pilot UAV-based workflow that links reality capture, immersive scenario comparison, and supplementary behavioral evidence within one process. Full article

The aim of this study is to address the limitations of fixed parameters and poor adaptability in traditional damage models for damage assessment of reinforced concrete (RC) columns under variable axial load. An improved damage model considering the influence of variable axial load was proposed herein. Based on quasi−static tests of RC columns under variable axial load, a fiber finite element model was established, and its reliability was verified using experimental data. The limitations of classical damage models were systematically analyzed, and the quantitative relationship between core parameters and axial load ratio was derived via numerical simulation of multi−level axial load ratio working conditions, on the basis of which the traditional model was modified. The applicability of the improved model was evaluated through full factorial combination working conditions, and the quantitative correlation among damage indices, stiffness degradation, and load−bearing capacity degradation was established. The results indicate that the improved model addresses the limitation of fixed parameters of traditional models, maintains stable calculation accuracy for circular RC columns under the investigated ranges of axial load ratio, shear−span ratio, and reinforcement ratio, and enables quantitative prediction of mechanical properties based on the damage index. Full article

This research proposes a conceptual framework that employs generative artificial intelligence (AI) to automatically generate dynamic biomimetic façade designs for reducing building carbon emissions. Biomimetic façades show strong carbon-reduction potential; however, their application remains limited by interdisciplinary requirements and time-intensive optimization processes. Existing studies primarily rely on traditional multi-objective optimization for energy performance, while machine learning integration and carbon-oriented evaluation remain limited in biomimetic façade research. To address this gap, this study proposes an AI system for biomimetic façade generation in tropical climates by combining reinforcement learning–based multi-objective optimization with deep learning–based parameter prediction models. A carbon payback assessment method integrating operational and embodied carbon is further proposed to evaluate carbon reduction performance. Preliminary validation through pilot experiments and K-fold cross-validation achieved an average RMSE of 8.7% and an average R² value of 0.547, while façade parameter prediction for new building conditions could be completed within approximately 10 s. Simulated cases also indicated that the generated façade strategies generally remained within predefined carbon payback thresholds under different material configurations. The framework supports carbon-oriented biomimetic façade design and early-stage low-carbon design decision-making. Full article

Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article

The diagnostic field of prostate cancer (PCa) has undergone a significant evolution with the widespread integration of multiparametric magnetic resonance imaging (mpMRI) and mpMRI-targeted biopsies (TBx). This approach has been shown to improve the detection of clinically significant prostate cancer (csPCa) while reducing the overdiagnosis of low-risk disease. However, a conceptual and clinical challenge, which can be referred to as the “Precision Paradox,” has emerged. By directing biopsy cores almost exclusively into the most suspicious MRI lesions, clinicians may inadvertently overrepresent the biological significance of a limited high-grade component. This can lead to grade migration and pathological downgrading at the time of radical prostatectomy (RP). Although downgrading does not automatically equate to clinical overtreatment, it introduces prognostic uncertainty that complicates risk stratification for active surveillance (AS) and focal therapy. This conceptual commentary provides a critical perspective on this diagnostic issue. We synthesize recent meta-analyses to evaluate the true rates of grade mismatch associated with TBx and combined biopsy approaches. Furthermore, we discuss the spatial limitations of biopsy sampling, the pathological mechanisms driving grade discordance, and the clinical relevance of minor high-grade components such as cribriform architecture. Finally, we highlight the role of multi-omics and validated genomic biomarkers in risk models, ultimately fostering improved shared decision-making in the modern mpMRI era. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

Colorectal cancer is the third most commonly diagnosed cancer worldwide, and early detection of polyps via colonoscopy is essential for improving patient survival. However, automatic polyp segmentation faces three key challenges: balancing global context with local detail, delineating ambiguous boundaries under low contrast, and handling large variations in polyp size and morphology. To address these challenges, we propose WMA-Net, a Wavelet-Guided Mamba-Attention Network that uses wavelet-domain semantic–boundary separation as the organizing design principle. Rather than introducing a new individual operator, the contribution lies in how existing components—wavelet decomposition, Mamba state space modeling, multi-directional pixel difference convolution, and uncertainty-aware reverse attention—are combined and coordinated within one boundary-aware framework. The architecture integrates pixel difference convolution for multi-directional edge detection, frequency-selective cross-scale fusion with dual-stream wavelet-domain processing, Mamba-based multi-scale aggregation with linear complexity, and uncertainty-aware progressive boundary refinement. Extensive experiments on five public polyp benchmarks demonstrate state-of-the-art performance on four out of five datasets. On the seen datasets, WMA-Net achieves mean Dice scores of 94.4% on CVC-ClinicDB and 93.6% on Kvasir-SEG. On the unseen datasets, WMA-Net attains 91.7% on CVC-300, 82.3% on CVC-ColonDB, and 83.8% on ETIS-LaribPolypDB, demonstrating robust cross-dataset generalization. Comprehensive ablation studies validate the effectiveness and synergy of each proposed module. Full article

Accurate short-term traction load forecasting is crucial for optimizing railway operations. However, the strong fluctuations in high-speed railway loads and the general neglect of the physical relationships between adjacent substations in existing studies pose significant challenges to reliable short-term forecasting. To address these issues, this paper proposes a Lag-Adaptive Gradient Aware Network (LAGA-Net). Unlike isolated forecasting methods, LAGA-Net explicitly combines the physical information of train motion with deep learning methods to achieve collaborative load forecasting between adjacent traction substations (TSs). Specifically, it first calculates the cross-correlation coefficients of the load curves of adjacent TSs to quantify the train lag process and achieve load time-series alignment, effectively utilizing the historical load of upstream substations as prior information for load forecasting at this station. Based on this, a dual-stream gradient sensing encoder is proposed to capture the load amplitude and high-frequency pulses of the two TSs, improving the prediction accuracy of the model in highly volatile scenarios. Finally, an adaptive cross-attention mechanism based on Gaussian masks is designed to achieve spatiotemporal coupling and collaborative forecasting of the loads of two adjacent TSs using the aligned load representation information. Extensive experiments on real adjacent traction substation datasets demonstrate that LAGA-Net significantly outperforms existing state-of-the-art benchmark methods in terms of multi-step prediction and peak prediction accuracy, and exhibits strong robustness to operational uncertainties. 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

Brain–computer interfaces (BCIs) based on motor imagery (MI) aim to convert electroencephalographic (EEG) activity into reliable device commands across users and recording setups. However, low signal-to-noise ratio and strong inter-subject variability still limit true “plug-and-play” deployment without lengthy calibration. To address these challenges, we propose a multi-branch convolutional long short-term memory (CNN-LSTM) architecture that jointly performs multi-scale temporal feature extraction and within-trial sequence modeling. The model employs four parallel 1D convolutional branches with distinct kernel sizes, each followed by an LSTM module and late fusion, combined with group normalization and supervision over sequences of sub-windows within each trial. We evaluate the approach on the EEG Motor Movement/Imagery (EEGMMI) dataset from PhysioNet under strictly subject-independent conditions, and on the ISLab-MI Dataset, a 32-channel wearable-EEG collection designed to assess cross-setup robustness. On EEGMMI, the network achieves up to 82.63% accuracy for binary left/right MI and 74.10% for a four-class task using 4 s trials under 5-fold cross-validation, outperforming an EEGNet-style baseline by 1–10% depending on class count and window length. Under a leave-one-subject-out protocol, the model attains 74.9% mean accuracy for a three-class MI task. Zero-shot transfer to ISLab-MI yields 64.60% and 63.02% accuracy in three- and four-class settings, respectively, while brief subject-specific fine-tuning using only 20% of each session improves performance to 81.38% and 73.48%. These findings show that combining multi-scale convolutional feature extraction with explicit sequence modeling and robust normalization yields accurate, data-efficient, and portable MI decoders suitable for practical BCI applications. Full article

Accurate identification of HMs contamination in mine tailings is essential for understanding pollution and supporting remediation. However, conventional laboratory monitoring is labor-intensive, time-consuming, and spatially discontinuous, while UAV hyperspectral inversion is limited by spectral inconsistency, unstable performance under small-sample conditions, and insufficient interpretability. Here, we developed an interpretable UAV–laboratory synergistic framework for Cd and Pb mapping in the Yitong open-pit mine. Forty site-level soil samples, composited from 200 subsamples, were linked with UAV hyperspectral observations. Direct Standardization was used to harmonize UAV and laboratory spectra. A weighted voting ensemble (RF, GBDT, and XGBoost) achieved the best performance (R² = 0.85), outperforming the individual models and showing slightly higher stability than CNN (R² = 0.84). Environmental covariates (pH, SOM, SMC) revealed distinct metal-specific prediction patterns: Cd was mainly associated with pH–SOM interactions, whereas Pb was more strongly associated with SOM–SMC coupling. SHAP and Grad-CAM identified sensitive spectral regions, with Cd linked to the 440–580 nm range and Pb to the 720–740 nm range. Overall, this study provides an integrated framework that combines spectral transfer correction, stable multi-model inversion, and mechanism-oriented interpretability for HMs monitoring in complex mining environments. Full article

Cracks are among the most common pavement distresses, and their timely detection is crucial for road maintenance. Existing methods struggle to completely capture elongated and irregular cracks, often resulting in fragmented detection outputs, which leads to the inaccurate assessment of crack length and affects the reliability of pavement condition evaluation. To address this issue, this paper proposes a dual-path crack segmentation network that integrates CNN and Transformers. The CNN branch incorporates a dynamic multi-branch convolution module to enhance the directional perception and structural modeling of elongated cracks. The Transformer branch employs a lightweight DCNv4 module to replace traditional self-attention mechanisms, effectively capturing long-range dependencies while reducing computational complexity. A multi-path fusion module is designed to achieve the collaborative enhancement of dual-path features, improving the semantic representation of continuous crack regions. Additionally, a combined loss function of BCE and Dice is adopted to alleviate the severe class imbalance between crack and background pixels, further improving the completeness of crack segmentation. Experiments on four datasets, including CFD, DeepCrack537, Gaps384, and Crack500, demonstrate that the proposed model outperforms all compared methods in terms of F-score and mIoU. Ablation studies further validate the effectiveness of the dual-path architecture and its key modules in improving performance. Furthermore, in field validation on real road scenarios, the pavement condition index (PCI) calculated based on the proposed method shows an average deviation of only 0.81 compared to manually interpreted ground truth, demonstrating the practical value of continuous crack detection for pavement maintenance assessment. Full article

Fuzzy control decision-making (FCDM) is an intelligent paradigm that leverages linguistic variables to model knowledge, explicitly addressing epistemic uncertainty (incomplete system knowledge) and aleatoric uncertainty (stochastic noise), thereby enabling resolution of multi-objective optimization conflicts in complex industrial systems. Following the PRISMA protocol, this study conducts a systematic literature review of 123 peer-reviewed publications retrieved from IEEE Xplore, Web of Science, ScienceDirect, and Google Scholar over the period 1965–2026, with emphasis on developments in the past 15 years. Existing reviews predominantly focus on isolated subdomains (e.g., scheduling, maintenance, energy systems), lacking a unified cross-scenario synthesis and implementation framework for industrial FCDM. To address scalability challenges such as rule base explosion in high-dimensional spaces, the literature is analyzed with respect to hierarchical fuzzy architectures, rule pruning, and dimensionality reduction techniques. The primary contribution is a structured synthesis of FCDM mechanisms across four industrial domains, combined with a systematic examination of integration with Industrial Internet of Things (IIoT), Digital Twins, and Edge Analytics. Furthermore, a three-stage closed-loop framework is formalized as a unified optimization protocol and modular architecture with technical specifications for Industry 4.0 integration, comprising data preprocessing, fuzzy inference, and optimization-driven decision output with iterative feedback. Comparative evaluation against MILP, MPC, and DRL highlights the conditions under which FCDM provides superior robustness and interpretability. Full article

The thermodynamic prediction of the fast refueling process for vehicular hydrogen storage cylinders faces the complex problem of modeling multi-layer composite walls. Drawing on the series thermal resistance principle, this paper introduces an equivalent thermal conductivity approach, simplifying the multi-layer structure into homogeneous material. Combined with the real-gas-state equation, a coupled thermodynamic framework combining zero-dimensional gas dynamics and one-dimensional cylinder wall heat transfer is developed. The comparison and verification with the 70 MPa fast charging experimental data have demonstrated that the proposed model exhibits sufficient accuracy and robustness for the problem. By comparing the temperature rise changes of different volume type-III gas cylinders, it was found that the surface area-to-volume ratio (A/V) was the primary geometric factor—the key geometric parameter that governs the temperature rise behavior. Larger volume gas cylinders exhibit more significant temperature rise due to their lower heat dissipation efficiency. A further comparison of the thermal response characteristics between Type-III and Type-IV cylinders demonstrates that the equivalent thermal conductivity is the dominant parameter determining the temperature rise behavior: The lower this coefficient, the stronger the limitation on the cylinder’s heat dissipation capacity, and the more pronounced the temperature rise. The proposed method not only ensures accuracy but also reduces the complexity of the modeling process, providing an efficient theoretical tool for optimizing the refueling strategy and conducting thermal safety assessment of vehicular hydrogen storage systems. Full article

We present a composable, pipeline-based rules engine for detecting application-level intrusions in HTTP traffic with adaptive rule generation capabilities. Rules are expressed in JSON chain multi-step decoders (Base64, hex, XOR, zlib, gzip) with matching primitives (word boundaries, regular expressions, substring sets) to detect obfuscated payloads. To enable adaptation to novel attack patterns, we integrate a large language model (LLM) component as a second-opinion layer that automatically generates validated detection rules for previously unseen threats, combining the adaptability of machine learning with the interpretability of explicit rules. We evaluate the system on two standard benchmarks (CSIC 2010 and HttpParamsDataset) and present a head-to-head comparison with ModSecurity and the OWASP Core Rule Set, achieving 98.1% and 98.3% detection rates with F1 scores above 0.97 on both datasets while maintaining false positive rates below 0.51%. Full article